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https://github.com/patrick-llgc/learning-deep-learning
Paper reading notes on Deep Learning and Machine Learning
https://github.com/patrick-llgc/learning-deep-learning
3d-object-detection 3d-object-recognition cnn computer-vision deep-learning literature-review machine-learning medical medical-imaging paper paper-reading paper-review point-cloud reinforcement-learning
Last synced: 5 days ago
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Paper reading notes on Deep Learning and Machine Learning
- Host: GitHub
- URL: https://github.com/patrick-llgc/learning-deep-learning
- Owner: patrick-llgc
- Created: 2017-04-02T05:29:55.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-11-29T15:28:50.000Z (about 2 months ago)
- Last Synced: 2025-01-17T07:01:55.882Z (5 days ago)
- Topics: 3d-object-detection, 3d-object-recognition, cnn, computer-vision, deep-learning, literature-review, machine-learning, medical, medical-imaging, paper, paper-reading, paper-review, point-cloud, reinforcement-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 87.5 MB
- Stars: 1,147
- Watchers: 113
- Forks: 176
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Paper notes
This repository contains my paper reading notes on deep learning and machine learning. It is inspired by [Denny Britz](https://github.com/dennybritz/deeplearning-papernotes) and [Daniel Takeshi](https://github.com/DanielTakeshi/Paper_Notes). A minimalistic webpage generated with Github io can be found [here](https://patrick-llgc.github.io/Learning-Deep-Learning/).
## About me
My name is [Patrick Langechuan Liu](https://www.linkedin.com/in/patrick-llgc/). After about a decade of education and research in physics, I found my passion in deep learning and autonomous driving.
## What to read
If you are new to deep learning in computer vision and don't know where to start, I suggest you spend your first month or so dive deep into [this list of papers](start/first_cnn_papers.md). I did so ([see my notes](start/first_cnn_papers_notes.md)) and it served me well.
Here is [a list of trustworthy sources of papers](trusty.md) in case I ran out of papers to read.
## My review posts by topics
I regularly update [my blog in Toward Data Science](https://medium.com/@patrickllgc).
- [BEV Perception in Mass Production Autonomous Driving](https://towardsdatascience.com/bev-perception-in-mass-production-autonomous-driving-c6e3f1e46ae0)
- [Challenges of Mass Production Autonomous Driving in China](https://towardsdatascience.com/challenges-of-mass-production-autonomous-driving-in-china-407c7e2dc5d8)
- [Vision-centric Semantic Occupancy Prediction for Autonomous Driving](https://towardsdatascience.com/vision-centric-semantic-occupancy-prediction-for-autonomous-driving-16a46dbd6f65) ([related paper notes](topics/topic_occupancy_network.md))
- [Drivable Space in Autonomous Driving — The Industry](https://medium.com/@patrickllgc/drivable-space-in-autonomous-driving-the-industry-7a4624b94d41)
- [Drivable Space in Autonomous Driving — The Academia](https://towardsdatascience.com/drivable-space-in-autonomous-driving-a-review-of-academia-ef1a6aa4dc15)
- [Drivable Space in Autonomous Driving — The Concept](https://towardsdatascience.com/drivable-space-in-autonomous-driving-the-concept-df699bb8682f)
- [Monocular BEV Perception with Transformers in Autonomous Driving](https://towardsdatascience.com/monocular-bev-perception-with-transformers-in-autonomous-driving-c41e4a893944) ([related paper notes](topics/topic_transformers_bev.md))
- [Illustrated Differences between MLP and Transformers for Tensor Reshaping in Deep Learning](https://towardsdatascience.com/illustrated-difference-between-mlp-and-transformers-for-tensor-reshaping-52569edaf89)
- [Monocular 3D Lane Line Detection in Autonomous Driving](https://towardsdatascience.com/monocular-3d-lane-line-detection-in-autonomous-driving-4d7cdfabf3b6) ([related paper notes](topics/topic_3d_lld.md))
- [Deep-Learning based Object detection in Crowded Scenes](https://towardsdatascience.com/deep-learning-based-object-detection-in-crowded-scenes-1c9fddbd7bc4) ([related paper notes](topics/topic_crowd_detection.md))
- [Monocular Bird’s-Eye-View Semantic Segmentation for Autonomous Driving](https://towardsdatascience.com/monocular-birds-eye-view-semantic-segmentation-for-autonomous-driving-ee2f771afb59) ([related paper notes](topics/topic_bev_segmentation.md))
- [Deep Learning in Mapping for Autonomous Driving](https://towardsdatascience.com/deep-learning-in-mapping-for-autonomous-driving-9e33ee951a44)
- [Monocular Dynamic Object SLAM in Autonomous Driving](https://towardsdatascience.com/monocular-dynamic-object-slam-in-autonomous-driving-f12249052bf1)
- [Monocular 3D Object Detection in Autonomous Driving — A Review](https://towardsdatascience.com/monocular-3d-object-detection-in-autonomous-driving-2476a3c7f57e)
- [Self-supervised Keypoint Learning — A Review](https://towardsdatascience.com/self-supervised-keypoint-learning-aade18081fc3)
- [Single Stage Instance Segmentation — A Review](https://towardsdatascience.com/single-stage-instance-segmentation-a-review-1eeb66e0cc49)
- [Self-paced Multitask Learning — A Review](https://towardsdatascience.com/self-paced-multitask-learning-76c26e9532d0)
- [Convolutional Neural Networks with Heterogeneous Metadata](https://towardsdatascience.com/convolutional-neural-networks-with-heterogeneous-metadata-2af9241218a9)
- [Lifting 2D object detection to 3D in autonomous driving](https://towardsdatascience.com/geometric-reasoning-based-cuboid-generation-in-monocular-3d-object-detection-5ee2996270d1)
- [Multimodal Regression](https://towardsdatascience.com/anchors-and-multi-bin-loss-for-multi-modal-target-regression-647ea1974617)
- [Paper Reading in 2019](https://towardsdatascience.com/the-200-deep-learning-papers-i-read-in-2019-7fb7034f05f7?source=friends_link&sk=7628c5be39f876b2c05e43c13d0b48a3)
## 2024-11 (1)
- [On the Opportunities and Risks of Foundation Models](https://arxiv.org/abs/2108.07258) [[Notes](paper_notes/opportunities_foundation_models.md)]
## 2024-06 (8)
- [LINGO-1: Exploring Natural Language for Autonomous Driving](https://wayve.ai/thinking/lingo-natural-language-autonomous-driving/) [[Notes](paper_notes/lingo_1.md)] [Wayve, open-loop world model]
- [LINGO-2: Driving with Natural Language](https://wayve.ai/thinking/lingo-2-driving-with-language/) [[Notes](paper_notes/lingo_2.md)] [Wayve, closed-loop world model]
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://arxiv.org/abs/2406.09246) [open source RT-2]
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://arxiv.org/abs/2306.07962) CoRL 2023 [Simple non-learning based baseline]
- [QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving](https://arxiv.org/abs/2404.01486) [Waabi]
- [MPDM: Multipolicy decision-making in dynamic, uncertain environments for autonomous driving](https://ieeexplore.ieee.org/document/7139412) [[Notes](paper_notes/mpdm.md)] ICRA 2015 [Behavior planning, UMich, May Autonomy]
- [MPDM2: Multipolicy Decision-Making for Autonomous Driving via Changepoint-based Behavior Prediction](https://www.roboticsproceedings.org/rss11/p43.pdf) [[Notes](paper_notes/mpdm2.md)] RSS 2015 [Behavior planning]
- [MPDM3: Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment](https://link.springer.com/article/10.1007/s10514-017-9619-z) RSS 2017 [Behavior planning]
- [EUDM: Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching](https://arxiv.org/abs/2003.02746) [[Notes](paper_notes/eudm.md)] ICRA 2020 [Wenchao Ding, Shaojie Shen, Behavior planning]
- [TPP: Tree-structured Policy Planning with Learned Behavior Models](https://arxiv.org/abs/2301.11902) ICRA 2023 [Marco Pavone, Nvidia, Behavior planning]
- [MARC: Multipolicy and Risk-aware Contingency Planning for Autonomous Driving](https://arxiv.org/abs/2308.12021) [[Notes](paper_notes/marc.md)] RAL 2023 [Shaojie Shen, Behavior planning]
- [EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments](https://arxiv.org/abs/2108.07993) TRO 2021 [Wenchao Ding, encyclopedia of pnc]
- [trajdata: A Unified Interface to Multiple Human Trajectory Datasets](https://arxiv.org/abs/2307.13924) NeurIPS 2023 [Marco Pavone, Nvidia]
- [Optimal Vehicle Trajectory Planning for Static Obstacle Avoidance using Nonlinear Optimization](https://arxiv.org/abs/2307.09466) [Xpeng]
- [Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles](https://arxiv.org/abs/1910.04586) [[Notes](paper_notes/joint_learned_bptp.md)] IROS 2019 Oral [Uber ATG, behavioral planning, motion planning]
- [Enhancing End-to-End Autonomous Driving with Latent World Model](https://arxiv.org/abs/2406.08481)
- [OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments](https://arxiv.org/abs/2312.09243) [Jiwen Lu]
- [RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision](https://arxiv.org/abs/2309.09502) ICRA 2024
- [EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision](https://arxiv.org/pdf/2311.02077) [Sanja, Marco, NV]
- [FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation](https://opendrivelab.com/e2ead/AD23Challenge/Track_3_NVOCC.pdf?=&linkId=100000205404832)
- [Trajeglish: Traffic Modeling as Next-Token Prediction](https://arxiv.org/abs/2312.04535) ICLR 2024
- [Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks](https://arxiv.org/pdf/2106.13052) ITSC 2021
- [Learning-Based Approach for Online Lane Change Intention Prediction](https://ieeexplore.ieee.org/document/6629564/) IV 2013 [SVM, LC intention prediction]
- [Traffic Flow-Based Crowdsourced Mapping in Complex Urban Scenario](https://ieeexplore.ieee.org/document/10171417) RAL 2023 [Wenchao Ding, Huawei, crowdsourced map]
- [FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow](https://arxiv.org/abs/2305.01622) ICRA 2023
- [Hybrid A-star: Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments](https://www.semanticscholar.org/paper/Path-Planning-for-Autonomous-Vehicles-in-Unknown-Dolgov-Thrun/0e8c927d9c2c46b87816a0f8b7b8b17ed1263e9c) IJRR 2010 [Dolgov, Thrun, Searching]
- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame](https://www.semanticscholar.org/paper/Optimal-trajectory-generation-for-dynamic-street-in-Werling-Ziegler/6bda8fc13bda8cffb3bb426a73ce5c12cc0a1760) ICRA 2010 [Werling, Thrun, Sampling] [MUST READ for planning folks]
- [Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method](https://ieeexplore.ieee.org/document/9703250) TITS 2022
- [Baidu Apollo EM Motion Planner](https://arxiv.org/abs/1807.08048) [[Notes](paper_notes/apollo_em_planner.md)][Optimization]
- [基于改进混合A*的智能汽车时空联合规划方法](https://www.qichegongcheng.com/CN/abstract/abstract1500.shtml) 汽车工程: 规划&决策2023年 [Joint optimization, search]
- [Enable Faster and Smoother Spatio-temporal Trajectory Planning for Autonomous Vehicles in Constrained Dynamic Environment](https://journals.sagepub.com/doi/abs/10.1177/0954407020906627) JAE 2020 [Joint optimization, search]
- [Focused Trajectory Planning for Autonomous On-Road Driving](https://www.ri.cmu.edu/pub_files/2013/6/IV2013-Tianyu.pdf) IV 2013 [Joint optimization, Iteration]
- [SSC: Safe Trajectory Generation for Complex Urban Environments Using Spatio-Temporal Semantic Corridor](https://arxiv.org/abs/1906.09788) RAL 2019 [Joint optimization, SSC, Wenchao Ding, Motion planning]
- [AlphaGo: Mastering the game of Go with deep neural networks and tree search](https://www.nature.com/articles/nature16961) [[Notes](paper_notes/alphago.md)] Nature 2016 [DeepMind, MTCS]
- [AlphaZero: A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play](https://www.science.org/doi/full/10.1126/science.aar6404) Science 2017 [DeepMind]
- [MuZero: Mastering Atari, Go, chess and shogi by planning with a learned model](https://www.nature.com/articles/s41586-020-03051-4) Nature 2020 [DeepMind]
- [Grandmaster-Level Chess Without Search](https://arxiv.org/abs/2402.04494) [DeepMind]
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://arxiv.org/abs/1610.03295) [MobileEye, desire and traj optimization]
- [Comprehensive Reactive Safety: No Need For A Trajectory If You Have A Strategy](https://arxiv.org/abs/2207.00198) IROS 2022 [Da Fang, Qcraft]
- [BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning](https://arxiv.org/abs/2310.10357) AAAI 2024
- [LLM-MCTS: Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://arxiv.org/abs/2305.14078) NeurIPS 2023
- [Hivt: Hierarchical vector transformer for multi-agent motion prediction](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_HiVT_Hierarchical_Vector_Transformer_for_Multi-Agent_Motion_Prediction_CVPR_2022_paper.pdf) CVPR 2022 [Zikang Zhou, agent-centric, motion prediction]
- [QCNet: Query-Centric Trajectory Prediction](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf) [[Notes](paper_notes/qcnet.md)] CVPR 2023 [Zikang Zhou, scene-centric, motion prediction]
## 2024-03 (11)
- [Genie: Generative Interactive Environments](https://arxiv.org/abs/2402.15391) [[Notes](paper_notes/genie.md)] [DeepMind, World Model]
- [DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving](https://arxiv.org/abs/2309.09777) [[Notes](paper_notes/drive_dreamer.md)] [Jiwen Lu, World Model]
- [WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens](https://arxiv.org/abs/2401.09985) [[Notes](paper_notes/world_dreamer.md)] [Jiwen Lu, World Model]
- [VideoPoet: A Large Language Model for Zero-Shot Video Generation](https://arxiv.org/abs/2312.14125) [Like sora, but LLM, NOT world model]
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2304.08818) [[Notes](paper_notes/video_ldm.md)] CVPR 2023 [Sanja, Nvidia, VideoLDM, Video prediction]
- [Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos](https://arxiv.org/abs/2206.11795) NeurIPS 2022 [[Notes](paper_notes/vpt.md)] [OpenAI]
- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://arxiv.org/abs/2206.08853) NeurIPS 2022 [NVidia, Outstanding paper award]
- [Humanoid Locomotion as Next Token Prediction](https://arxiv.org/abs/2402.19469) [[Notes](paper_notes/locomotion_next_token_pred.md)] [Berkeley, EAI]
- [RPT: Robot Learning with Sensorimotor Pre-training](https://arxiv.org/abs/2306.10007) [[Notes](paper_notes/rpt.md)] CoRL 2023 Oral [Berkeley, EAI]
- [MVP: Real-World Robot Learning with Masked Visual Pre-training](https://arxiv.org/abs/2210.03109) [[Notes](paper_notes/mvp.md)] CoRL 2022 [Berkeley, EAI]
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://arxiv.org/abs/2202.02005) [[Notes](paper_notes/bc_z.md)] CoRL 2021 [Eric Jang, 1X]
- [GenAD: Generalized Predictive Model for Autonomous Driving](https://arxiv.org/abs/2403.09630) [[Notes](paper_notes/genad.md)] CVPR 2024
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://arxiv.org/abs/1810.02890) [DAgger]
- [DriveGAN: Towards a Controllable High-Quality Neural Simulation](https://arxiv.org/abs/2104.15060) [[Notes](paper_notes/drive_gan.md)] CVPR 2021 oral [Nvidia, Sanja]
- [VideoGPT: Video Generation using VQ-VAE and Transformers](https://arxiv.org/abs/2104.10157) [[Notes](paper_notes/videogpt.md)] [Pieter Abbeel]
- [LLM, Vision Tokenizer and Vision Intelligence, by Lu Jiang](https://mp.weixin.qq.com/s/Hamz5XMT1tSZHKdPaCBTKg) [[Notes](paper_notes/llm_vision_intel.md)] [Interview Lu Jiang]
- [AV2.0: Reimagining an autonomous vehicle](https://arxiv.org/abs/2108.05805) [[Notes](paper_notes/av20.md)] [Wayve, Alex Kendall]
- [Simulation for E2E AD](https://www.youtube.com/watch?v=8fivoXbT1Ao&ab_channel=Wayve) [Wayve, Tech Sharing, E2E]
- [E2E lateral planning](https://blog.comma.ai/end-to-end-lateral-planning/) [Comma.ai, E2E planning]
- [Learning and Leveraging World Models in Visual Representation Learning](https://arxiv.org/abs/2403.00504) [LeCun, JEPA series]
- [LVM: Sequential Modeling Enables Scalable Learning for Large Vision Models](https://arxiv.org/abs/2312.00785) [Large Vision Models, Jitendra Malik]
- [LWM: World Model on Million-Length Video And Language With RingAttention](https://arxiv.org/abs/2402.08268) [Pieter Abbeel]
- [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving](https://arxiv.org/abs/2311.16038) [Jiwen Lu, World Model]
- [GenAD: Generative End-to-End Autonomous Driving](https://arxiv.org/abs/2402.11502)
- [TCP: Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://arxiv.org/abs/2206.08129) NeurIPS 2022 [E2E planning, Hongyang]
- [Transfuser: Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://arxiv.org/abs/2104.09224) CVPR 2021 [E2E planning, Geiger]
- [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://arxiv.org/abs/2310.01957) [Wayve, LLM + AD]
- [LingoQA: Video Question Answering for Autonomous Driving](https://arxiv.org/abs/2312.14115) [Wayve, LLM + AD]
- [Panacea: Panoramic and Controllable Video Generation for Autonomous Driving](https://arxiv.org/abs/2311.16813) CVPR 2024 [Megvii]
- [PlanT: Explainable Planning Transformers via Object-Level Representations](https://arxiv.org/abs/2210.14222) CoRL 2022
- [Scene as Occupancy](https://arxiv.org/abs/2306.02851) ICCV 2023
- [AD-MLP: Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes](https://arxiv.org/abs/2305.10430) [Baidu]
- [The Shift from Models to Compound AI Systems](https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/)
- [Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach](https://arxiv.org/abs/2108.08265) ICCV 2021
- [Learning from All Vehicles](https://arxiv.org/abs/2203.11934) CVPR 2022
- [LBC: Learning by Cheating](https://arxiv.org/abs/1912.12294) CoRL 2019
- [Learning to drive from a world on rails](https://arxiv.org/abs/2105.00636) ICCV 2021 oral [Philipp Krähenbühl]
- [Learning from All Vehicles](https://arxiv.org/abs/2203.11934) CVPR 2022 [Philipp Krähenbühl]
- [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://arxiv.org/abs/2402.13243) [Horizon]
- [VQ-VAE: Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) NeurIPS 2017 [Image Tokenizer]
- [VQ-GAN: Taming Transformers for High-Resolution Image Synthesis](https://arxiv.org/abs/2012.09841) CVPR 2021 [Image Tokenizer]
- [ViT-VQGAN: Vector-quantized Image Modeling with Improved VQGAN](https://arxiv.org/abs/2110.04627) ICLR 2022 [Image Tokenizer]
- [MaskGIT: Masked Generative Image Transformer](https://arxiv.org/abs/2202.04200) CVPR 2022 [LLM, non-autoregressive]
- [MAGVIT: Masked Generative Video Transformer](https://arxiv.org/abs/2212.05199) CVPR 2023 highlight [Video Tokenizer]
- [MAGVIT-v2: Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation](https://arxiv.org/abs/2310.05737) ICLR 2024 [Video Tokenizer]
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://arxiv.org/abs/2402.17177) [Reverse Engineering of Sora]
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/abs/2112.06905) ICML 2022 [MoE, LLM]
- [Lifelong Language Pretraining with Distribution-Specialized Experts](https://arxiv.org/abs/2305.12281) ICML 2023 [MoE, LLM]
- [DriveLM: Drive on Language](https://arxiv.org/abs/2312.14150) [Hongyang Li]
- [MotionLM: Multi-Agent Motion Forecasting as Language Modeling](https://arxiv.org/abs/2309.16534) ICCV 2023 [Waymo, LLM + AD]
- [AD-MLP: Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes](https://arxiv.org/abs/2305.10430) [No perception]
- CubeLLM: align 2D/3D with language
- EmerNeRF: ICLR 2024
- A Language Agent for Autonomous Driving
- [Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal]
- [DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation](https://arxiv.org/abs/2403.06845)
- [DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving](https://arxiv.org/abs/2405.04390) CVPR 2024 [Zheng Zhu]
- [Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond](https://arxiv.org/abs/2405.03520) [Zheng Zhu]
## 2024-02 (7)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://arxiv.org/abs/2306.16927) [[Notes](paper_notes/e2e_review_hongyang.md)] [Hongyang Li, Shanghai AI labs]
- [DriveVLM: The convergence of Autonomous Driving and Large Vision-Language Models](https://arxiv.org/abs/2402.12289) [[Notes](paper_notes/drivevlm.md)] [Hang Zhao]
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://arxiv.org/abs/2310.01412) [[Notes](paper_notes/drivegpt4.md)] [HKU]
- [GAIA-1: A Generative World Model for Autonomous Driving](https://arxiv.org/abs/2309.17080) [[Notes](paper_notes/gaia_1.md)] [Wayve, vision foundation model]
- [ADriver-I: A General World Model for Autonomous Driving](https://arxiv.org/abs/2311.13549) [[Notes](paper_notes/adriver_i.md)] [Megvii, Xiangyu]
- [Drive-WM: Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://arxiv.org/abs/2311.17918) [[Notes](paper_notes/drive_wm.md)]
- [X]() [[Notes](paper_notes/x.md)] [E2E planning]
## 2023-12 (4)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://arxiv.org/abs/2306.17582) [[Notes](paper_notes/prompt_craft.md)] [Microsoft, LLM for robotics]
- [RoboVQA: Multimodal Long-Horizon Reasoning for Robotics](https://arxiv.org/abs/2311.00899) [[Notes](paper_notes/robovqa.md)] [Google DeepMind, LLM for robotics]
- [ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application](https://ieeexplore.ieee.org/document/10235949) [Microsoft Robotics]
- [GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration](https://arxiv.org/abs/2311.12015) [[Notes](paper_notes/gpt4v_robotics.md)] [LLM for robotics, Microsoft Robotics]
- [LLM-Brain: LLM as A Robotic Brain: Unifying Egocentric Memory and Control](https://arxiv.org/abs/2304.09349) [[Notes](paper_notes/llm_brain.md)]
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://arxiv.org/abs/2305.16291) [[Notes](paper_notes/voyager.md)] [Reasoning Critique, Linxi Jim Fan]
## 2023-09 (3)
- [RetNet: Retentive Network: A Successor to Transformer for Large Language Models](https://arxiv.org/abs/2307.08621) [[Notes](paper_notes/retnet.md)] [MSRA]
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://arxiv.org/abs/2006.16236) [[Notes](paper_notes/transformers_are_rnns.md)] ICML 2020 [Linear attention]
- [AFT: An Attention Free Transformer](https://arxiv.org/abs/2105.14103) [[Notes](paper_notes/aft.md)] [Apple]
## 2023-08 (3)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://arxiv.org/abs/2212.06817) [[Notes](paper_notes/rt1.md)] [DeepMind]
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://robotics-transformer2.github.io/assets/rt2.pdf) [[Notes](paper_notes/rt2.md)] [DeepMind, end-to-end visuomotor]
- [RWKV: Reinventing RNNs for the Transformer Era](https://arxiv.org/abs/2305.13048) [[Notes](paper_notes/rwkv.md)]
## 2023-07 (6)
- [MILE: Model-Based Imitation Learning for Urban Driving](https://arxiv.org/abs/2210.07729) [[Notes](paper_notes/mile.md)] NeurIPS 2022 [Alex Kendall]
- [PaLM-E: An embodied multimodal language model](https://arxiv.org/abs/2303.03378) [[Notes](paper_notes/palm_e.md)] [Google Robotics]
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://voxposer.github.io/voxposer.pdf) [[Notes](paper_notes/voxposer.md)] [Feifei Li]
- [CaP: Code as Policies: Language Model Programs for Embodied Control](https://arxiv.org/abs/2209.07753) [[Notes](paper_notes/cap.md)] [[Project](https://code-as-policies.github.io/)]
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://arxiv.org/abs/2209.11302) ICRA 2023
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://arxiv.org/abs/2305.05658) [[Notes](paper_notes/tidybot.md)] [[Project](https://tidybot.cs.princeton.edu/)]
- [SayCan: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://arxiv.org/abs/2204.01691) [[Notes](paper_notes/saycan.md)] [[Project](https://say-can.github.io/)]
## 2023-06 (5)
- [End-to-end review by Shanghai AI Labs](https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving)
- [Pix2seq v2: A Unified Sequence Interface for Vision Tasks](https://arxiv.org/abs/2206.07669) [[Notes](paper_notes/pix2seq_v2.md)] NeurIPS 2022 [Geoffrey Hinton]
- 🦩 [Flamingo: a Visual Language Model for Few-Shot Learning](https://arxiv.org/abs/2204.14198) [[Notes](paper_notes/flamingo.md)] NeurIPS 2022 [DeepMind]
- 😼 [Gato: A Generalist Agent](https://arxiv.org/abs/2205.06175) [[Notes](paper_notes/gato.md)] TMLR 2022 [DeepMind]
- [BC-SAC: Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios](https://arxiv.org/abs/2212.11419) [[Notes](paper_notes/bc_sac.md)] NeurIPS 2022 [Waymo]
- [MGAIL-AD: Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving](https://arxiv.org/abs/2210.09539) [[Notes](paper_notes/mgail_ad.md)] IROS 2022 [Waymo]
## 2023-05 (7)
- [SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving](https://arxiv.org/abs/2303.09551) [[Notes](paper_notes/surroundocc.md)] [Occupancy Network, Wei Yi, Jiwen Lu]
- [Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving](https://arxiv.org/abs/2304.14365) [[Notes](paper_notes/occ3d.md)] [Occupancy Network, Zhao Hang]
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://arxiv.org/abs/1812.03828) CVPR 2019 [[Notes](paper_notes/occupancy_networks.md)] [Andreas Geiger]
- [OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction](https://arxiv.org/abs/2304.05316) [Occupancy Network, PhiGent]
- [Pix2seq: A Language Modeling Framework for Object Detection](https://arxiv.org/abs/2109.10852) [[Notes](paper_notes/pix2seq.md)] ICLR 2022 [Geoffrey Hinton]
- [VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks](https://arxiv.org/abs/2305.11175) [[Notes](paper_notes/vision_llm.md)] [Jifeng Dai]
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://arxiv.org/abs/2303.17580) [[Notes](paper_notes/hugging_gpt.md)]
## 2023-04 (1)
- [UniAD: Planning-oriented Autonomous Driving](https://arxiv.org/abs/2212.10156) [[Notes](paper_notes/uniad.md)] CVPR 2023 best paper [BEV, e2e, Hongyang Li]
## 2023-03 (5)
- [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774) [[Notes](paper_notes/gpt4.md)] [OpenAI, GPT]
- [OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception](https://arxiv.org/abs/2303.03991) [[Notes](paper_notes/openoccupancy.md)] [Occupancy Network, Jiwen Lu]
- [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://arxiv.org/abs/2302.12251) [[Note](paper_notes/voxformer.md)] CVPR 2023 highlight [Occupancy Network, Nvidia]
- [MonoScene: Monocular 3D Semantic Scene Completion](https://arxiv.org/abs/2112.00726) CVPR 2022 [[Notes](paper_notes/monoscene.md)] [Occupancy Network, single cam]
- [CoReNet: Coherent 3D scene reconstruction from a single RGB image](https://arxiv.org/abs/2004.12989) [[Notes](paper_notes/corenet.md)] ECCV 2020 oral
## 2023-02 (4)
- [Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning](https://arxiv.org/abs/2211.04325) [[Notes](paper_notes/out_of_data.md)] [Epoch.ai industry report]
- [Codex: Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374) [[Notes](paper_notes/codex.md)] [GPT, OpenAI]
- [InstructGPT: Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) [[Notes](paper_notes/instructgpt.md)] [GPT, OpenAI]
- [TPVFormer: Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction](https://arxiv.org/abs/2302.07817) [[Notes](paper_notes/tpvformer.md)] CVPR 2023 [Occupancy Network, Jiwen Lu]
## 2023-01 (2)
- [PPGeo: Policy Pre-training for End-to-end Autonomous Driving via Self-supervised Geometric Modeling](https://arxiv.org/abs/2301.01006) [[Notes](paper_notes/ppgeo.md)] ICLR 2023
- [nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://arxiv.org/abs/2106.11810) [[Notes](paper_notes/nuplan.md)]
## 2022-11 (1)
- [M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction](https://arxiv.org/abs/2202.11884) [[Notes](paper_notes/m2i.md)] CVPR 2022
## 2022-10 (1)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://arxiv.org/abs/2209.05324) [[Notes](paper_notes/delving_bev.md)] [PJLab]
## 2022-09 (3)
- [ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries](https://arxiv.org/abs/2208.01582) [[Notes](paper_notes/vip3d.md)] [BEV, perception + prediction, Hang Zhao]
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://arxiv.org/abs/2208.14437) [[Notes](paper_notes/maptr.md)] [Horizon, BEVNet]
- [StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving](https://arxiv.org/abs/2206.00991) ICRA 2022
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://arxiv.org/abs/2105.03247) ECCV 2022 [Megvii, MOT]
- [Anchor DETR: Query Design for Transformer-Based Object Detection](https://arxiv.org/abs/2109.07107) [[Notes](paper_notes/anchor_detr.md)] AAAI 2022 [Megvii]
## 2022-08 (1)
- [HOME: Heatmap Output for future Motion Estimation](https://arxiv.org/abs/2105.10968) [[Notes](paper_notes/home.md)] ITSC 2021 [behavior prediction, Huawei Paris]
## 2022-07 (8)
- [PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark](https://arxiv.org/abs/2203.11089) [[Notes](paper_notes/persformer.md)] [BEVNet, lane line]
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://arxiv.org/abs/2206.08920) [[Notes](paper_notes/vectormapnet.md)] [BEVNet, LLD, Hang Zhao]
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://arxiv.org/abs/2203.05625) [[Notes](paper_notes/petr.md)] ECCV 2022 [BEVNet]
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://arxiv.org/abs/2206.01256) [[Notes](paper_notes/petrv2.md)] [BEVNet, MegVii]
- [M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation](https://arxiv.org/abs/2204.05088) [[Notes](paper_notes/m2bev.md)] [BEVNet, nvidia]
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://arxiv.org/abs/2206.10092) [[Notes](paper_notes/bevdepth.md)] [BEVNet, NuScenes SOTA, Megvii]
- [CVT: Cross-view Transformers for real-time Map-view Semantic Segmentation](https://arxiv.org/abs/2205.02833) [[Notes](paper_notes/cvt.md)] CVPR 2022 oral [UTAustin, Philipp]
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://arxiv.org/abs/2207.05844) [[Notes](paper_notes/wayformer.md)] [Behavior prediction, Waymo]
## 2022-06 (3)
- [BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection](https://arxiv.org/abs/2203.17054) [[Notes](paper_notes/bevdet4d.md)] [BEVNet]
- [BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving](https://arxiv.org/abs/2205.09743) [[Notes](paper_notes/beverse.md)] [Jiwen Lu, BEVNet, perception + prediction]
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://arxiv.org/abs/2205.13542) [[Notes](paper_notes/bevfusion.md)] [BEVNet, Han Song]
## 2022-03 (1)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://arxiv.org/abs/2203.17270) [[Notes](paper_notes/bevformer.md)] ECCV 2022 [BEVNet, Hongyang Li, Jifeng Dai]
## 2022-02 (1)
- [TNT: Target-driveN Trajectory Prediction](https://arxiv.org/abs/2008.08294) [[Notes](paper_notes/tnt.md)] CoRL 2020 [prediction, Waymo, Hang Zhao]
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://arxiv.org/abs/2108.09640) [[Notes](paper_notes/dense_tnt.md)] ICCV 2021 [prediction, Waymo, 1st place winner WOMD]
## 2022-01 (1)
- [Manydepth: The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://arxiv.org/abs/2104.14540) [[Notes](paper_notes/manydepth.md)] CVPR 2021 [monodepth, Niantic]
- [DEKR: Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression](https://arxiv.org/abs/2104.02300) [[Notes](paper_notes/dekr.md)] CVPR 2021
## 2021-12 (5)
- [BN-FFN-BN: Leveraging Batch Normalization for Vision Transformers](https://openaccess.thecvf.com/content/ICCV2021W/NeurArch/papers/Yao_Leveraging_Batch_Normalization_for_Vision_Transformers_ICCVW_2021_paper.pdf) [[Notes](paper_notes/bn_ffn_bn.md)] ICCVW 2021 [BN, transformers]
- [PowerNorm: Rethinking Batch Normalization in Transformers](https://arxiv.org/abs/2003.07845) [[Notes](paper_notes/powernorm.md)] ICML 2020 [BN, transformers]
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://arxiv.org/abs/2111.14973) [[Notes](paper_notes/multipath++.md)] ICRA 2022 [Waymo, behavior prediction]
- [BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View](https://arxiv.org/abs/2112.11790) [[Notes](paper_note/bevdet.md)]
- [Translating Images into Maps](https://arxiv.org/abs/2110.00966) [[Notes](paper_notes/translating_images_to_maps.md)] ICRA 2022 [BEVNet, transformers]
## 2021-11 (4)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://arxiv.org/abs/2110.06922) [[Notes](paper_notes/detr3d.md)] CoRL 2021 [BEVNet, transformers]
- [Robust-CVD: Robust Consistent Video Depth Estimation](https://arxiv.org/abs/2012.05901) CVPR 2021 oral [[website](https://robust-cvd.github.io/)]
- [MAE: Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) [[Notes](paper_notes/mae.md)] [Kaiming He, unsupervised learning]
- [SimMIM: A Simple Framework for Masked Image Modeling](https://arxiv.org/abs/2111.09886) [[Notes](paper_notes/simmim.md)] [MSRA, unsupervised learning, MAE]
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://arxiv.org/abs/2111.07832)
## 2021-10 (3)
- [STSU: Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://arxiv.org/abs/2110.01997) [[Notes](paper_notes/stsu.md)] ICCV 2021 [BEV feat stitching, Luc Van Gool]
- [PanopticBEV: Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images](https://arxiv.org/abs/2108.03227) [[Notes](paper_notes/panoptic_bev.md)] RAL 2022 [BEVNet, vertical/horizontal features]
- [NEAT: Neural Attention Fields for End-to-End Autonomous Driving](https://arxiv.org/abs/2109.04456) [[Notes](paper_notes/neat.md)] ICCV 2021 [[supplementary](http://www.cvlibs.net/publications/Chitta2021ICCV_supplementary.pdf)] [BEVNet]
## 2021-09 (11)
- [DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection?](https://arxiv.org/abs/2108.06417) [[Notes](paper_notes/dd3d.md)] ICCV 2021 [mono3D, Toyota]
- [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/abs/1911.09070) [[Notes](paper_notes/efficientdet.md)] CVPR 2020 [BiFPN, Tesla AI day]
- [PnPNet: End-to-End Perception and Prediction with Tracking in the Loop](https://arxiv.org/abs/2005.14711) [[Notes](paper_notes/pnpnet.md)] CVPR 2020 [Uber ATG]
- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://arxiv.org/abs/2101.06806) [[Notes](paper_notes/mp3.md)] CVPR 2021 [Uber, planning]
- [BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning](http://arxiv.org/abs/2110.04931) [[Notes](paper_notes/bevnet_sdca.md)] ICCV 2021 [BEVNet, surveillance]
- [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://arxiv.org/abs/2103.15297) [[Notes](paper_notes/lidar_rcnn.md)] CVPR 2021 [TuSimple, Naiyan Wang]
- [Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches](https://arxiv.org/abs/2102.05897) [[Notes](paper_notes/corner_case_vision_arxiv.md)] [corner cases]
- [Systematization of Corner Cases for Visual Perception in Automated Driving](https://ieeexplore.ieee.org/document/9304789) [[Notes](paper_notes/corner_case_vision_iv.md)] IV 2020 [corner cases]
- [An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving](https://arxiv.org/abs/2103.03678) [[Notes](paper_notes/corner_case_multisensor.md)] IV 2021 [corner cases]
- [PYVA: Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation](https://openaccess.thecvf.com/content/CVPR2021/html/Yang_Projecting_Your_View_Attentively_Monocular_Road_Scene_Layout_Estimation_via_CVPR_2021_paper.html) [[Notes](paper_notes/pyva.md)] CVPR 2021 [[Supplementary](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yang_Projecting_Your_View_CVPR_2021_supplemental.zip)] [BEVNet]
- [YOLOF: You Only Look One-level Feature](https://arxiv.org/abs/2103.09460) [[Notes](paper_notes/yolof.md)] CVPR 2021 [megvii]
- [Perceiving Humans: from Monocular 3D Localization to Social Distancing](https://arxiv.org/abs/2009.00984) [[Notes](paper_notes/perceiving_humans.md)] TITS 2021 [monoloco++]
- [PifPaf: Composite Fields for Human Pose Estimation](https://arxiv.org/abs/1903.06593) CVPR 2019
- [Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images](https://arxiv.org/abs/2108.03227) [BEVNet]
- [TransformerFusion: Monocular RGB Scene Reconstruction using Transformers](https://arxiv.org/abs/2107.02191)
- [Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation](https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Projecting_Your_View_Attentively_Monocular_Road_Scene_Layout_Estimation_via_CVPR_2021_paper.pdf) CVPR 2021
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://arxiv.org/abs/2104.09224) CVPR 2021
- [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152)
- [Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://arxiv.org/abs/2107.14160) CoRL 2021
## 2021-08 (11)
- [EgoNet: Exploring Intermediate Representation for Monocular Vehicle Pose Estimation](https://arxiv.org/abs/2011.08464) [[Notes](paper_notes/egonet.md)] CVPR 2021 [mono3D]
- [MonoEF: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach](https://arxiv.org/abs/2106.15796) [[Notes](paper_notes/monoef.md)] CVPR 2021 [mono3D]
- [GAC: Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://arxiv.org/abs/2102.00690) [[Notes](paper_notes/gac.md)] RAL 2021 [mono3D]
- [FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection](https://arxiv.org/abs/2104.10956) [[Notes](paper_notes/fcos3d.md)] NeurIPS 2020 [mono3D, senseTime]
- [GUPNet: Geometry Uncertainty Projection Network for Monocular 3D Object Detection](https://arxiv.org/abs/2107.13774) [[Notes](paper_notes/gupnet.md)] ICCV 2021 [mono3D, Wanli Ouyang]
- [DARTS: Differentiable Architecture Search](https://arxiv.org/abs/1806.09055) [[Notes](paper_notes/darts.md)] ICLR 2019 [VGG author]
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://arxiv.org/abs/1812.03443) [[Notes](paper_notes/fbnet.md)] CVPR 20219 [DARTS]
- [FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions](https://arxiv.org/abs/2004.05565) CVPR 2020
- [FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining](https://arxiv.org/abs/2006.02049) CVPR 2021
- [Perceiver: General Perception with Iterative Attention](https://arxiv.org/abs/2103.03206) [[Notes](paper_notes/perceiver.md)] ICML 2021 [transformers, multimodal]
- [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) [[Notes](paper_notes/perceiver_io.md)]
- [PillarMotion: Self-Supervised Pillar Motion Learning for Autonomous Driving](https://arxiv.org/abs/2104.08683) [[Notes](paper_notes/pillar_motion.md)] CVPR 2021 [Qcraft, Alan Yuille]
- [SimTrack: Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://arxiv.org/abs/2108.10312) [[Notes](paper_notes/simtrack.md)] ICCV 2019 [QCraft, Alan Yuille]
## 2021-07 (1)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://arxiv.org/abs/2107.06307) [[Notes](paper_notes/hdmapnet.md)] CVPR 2021 workshop [youtube video only, Li Auto]
## 2021-06 (2)
- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://arxiv.org/abs/2104.10490) [[Notes](paper_notes/fiery.md)] ICCV 2021 [BEVNet, perception + prediction]
- [Baidu's CNN seg](https://zhuanlan.zhihu.com/p/35034215) [[Notes](paper_notes/cnn_seg.md)]
## 2021-04 (5)
- [Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation](https://arxiv.org/abs/2012.15175) [[Notes](paper_notes/swahr.md)] CVPR 2021 [megvii]
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://arxiv.org/abs/1812.00324) CVPR 2019
- [The Overlooked Elephant of Object Detection: Open Set](https://openaccess.thecvf.com/content_WACV_2020/html/Dhamija_The_Overlooked_Elephant_of_Object_Detection_Open_Set_WACV_2020_paper.html) WACV 2021
- [Class-Agnostic Object Detection](https://arxiv.org/abs/2011.14204) WACV 2021
- [OWOD: Towards Open World Object Detection](https://arxiv.org/abs/2103.02603) [[Notes](paper_notes/owod.md)] CVPR 2021 oral
- [FsDet: Frustratingly Simple Few-Shot Object Detection](https://arxiv.org/abs/2003.06957) ICML 2020
- [MonoFlex: Objects are Different: Flexible Monocular 3D Object Detection](https://arxiv.org/abs/2104.02323) [[Notes](paper_notes/monoflex.md)] CVPR 2021 [mono3D, Jiwen Lu, cropped]
- [monoDLE: Delving into Localization Errors for Monocular 3D Object Detection](https://arxiv.org/abs/2103.16237) [[Notes](paper_notes/monodle.md)] CVPR 2021 [mono3D]
- [Exploring 2D Data Augmentation for 3D Monocular Object Detection](https://arxiv.org/abs/2104.10786)
- [OCM3D: Object-Centric Monocular 3D Object Detection](https://arxiv.org/abs/2104.06041) [mono3D]
- [FSM: Full Surround Monodepth from Multiple Cameras](https://arxiv.org/abs/2104.00152) [[Notes](paper_notes/fsm.md)] ICRA 2021 [monodepth, Xnet]
## 2021-03 (4)
- [CaDDN: Categorical Depth Distribution Network for Monocular 3D Object Detection](https://arxiv.org/abs/2103.01100) [[Notes](paper_notes/caddn.md)] CVPR 2021 oral [mono3D, BEVNet]
- [DSNT: Numerical Coordinate Regression with Convolutional Neural Networks](https://arxiv.org/abs/1801.07372) [[Notes](paper_notes/dsnt.md)] [differentiable spatial to numerical transform]
- [Soft-Argmax: Human pose regression by combining indirect part detection and contextual information](https://arxiv.org/abs/1710.02322)
- [INSTA-YOLO: Real-Time Instance Segmentation](https://arxiv.org/abs/2102.06777) [[Notes](paper_notes/insta_yolo.md)] ICML workshop 2020 [single stage instance segmentation]
- [CenterNet2: Probabilistic two-stage detection](https://arxiv.org/abs/2103.07461) [[Notes](paper_notes/centernet2.md)] [CenterNet, two-stage]
## 2021-01 (7)
- [Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection](https://arxiv.org/abs/2012.00257) [[Notes](paper_notes/confluence.md)] [NMS]
- [BoxInst: High-Performance Instance Segmentation with Box Annotations](https://arxiv.org/abs/2012.02310) [[Notes](paper_notes/boxinst.md)] CVPR 2021 [Chunhua Shen, Tian Zhi]
- [3DSSD: Point-based 3D Single Stage Object Detector](https://arxiv.org/abs/2002.10187) [[Notes](paper_notes/3dssd.md)] CVPR 2020
- [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697) [[Notes](paper_notes/repvgg.md)] [Megvii, Xiangyu Zhang, ACNet]
- [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://arxiv.org/abs/1908.03930) [[Notes](paper_notes/acnet.md)] ICCV 2019
- [BEV-Feat-Stitching: Understanding Bird's-Eye View Semantic HD-Maps Using an Onboard Monocular Camera](https://arxiv.org/abs/2012.03040) [[Notes](paper_notes/bev_feat_stitching.md)] [BEVNet, mono3D, Luc Van Gool]
- [PSS: Object Detection Made Simpler by Eliminating Heuristic NMS](https://arxiv.org/abs/2101.11782) [[Notes](paper_notes/pss.md)] [Transformer, DETR]
## 2020-12 (17)
- [DeFCN: End-to-End Object Detection with Fully Convolutional Network](https://arxiv.org/abs/2012.03544) [[Notes](paper_notes/defcn.md)] [Transformer, DETR]
- [OneNet: End-to-End One-Stage Object Detection by Classification Cost](https://arxiv.org/abs/2012.05780) [[Notes](paper_notes/onenet.md)] [Transformer, DETR]
- [Traffic Light Mapping, Localization, and State Detection for Autonomous Vehicles](http://driving.stanford.edu/papers/ICRA2011.pdf) [[Notes](paper_notes/tfl_stanford.md)] ICRA 2011 [traffic light, Sebastian Thrun]
- [Towards lifelong feature-based mapping in semi-static environments](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/43966.pdf) [[Notes](paper_notes/lifelong_feature_mapping_google.md)] ICRA 2016
- [How to Keep HD Maps for Automated Driving Up To Date](http://www.lewissoft.com/pdf/ICRA2020/1484.pdf) [[Notes](paper_notes/keep_hd_maps_updated_bmw.md)] ICRA 2020 [BMW]
- [Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection](https://arxiv.org/abs/2011.12885) [[Notes](paper_notes/gfocalv2.md)] CVPR 2021 [focal loss]
- [Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w9/Milz_Visual_SLAM_for_CVPR_2018_paper.pdf) [[Notes](paper_notes/vslam_for_ad.md)] CVPR 2018 workshop
- [Centroid Voting: Object-Aware Centroid Voting for Monocular 3D Object Detection](https://arxiv.org/abs/2007.09836) [[Notes](paper_notes/centroid_voting.md)] IROS 2020 [mono3D, geometry + appearance = distance]
- [Monocular 3D Object Detection in Cylindrical Images from Fisheye Cameras](https://arxiv.org/abs/2003.03759) [[Notes](paper_notes/mono3d_fisheye.md)] [GM Israel, mono3D]
- [DeepPS: Vision-Based Parking-Slot Detection: A DCNN-Based Approach and a Large-Scale Benchmark Dataset](https://cslinzhang.github.io/deepps/parkingslot.pdf) TIP 2018 [Parking slot detection, PS2.0 dataset]
- [PSDet: Efficient and Universal Parking Slot Detection](https://arxiv.org/abs/2005.05528) [[Notes](paper_notes/psdet.md)] IV 2020 [Zongmu, Parking slot detection]
- [PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning](https://arxiv.org/abs/2001.00138) [[Notes](paper_notes/patdnn.md)] ASPLOS 2020 [pruning]
- [Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://arxiv.org/abs/2011.08036) [[Notes](paper_notes/scaled_yolov4.md)] [yolo]
- [Yolov5 by Ultralytics](https://github.com/ultralytics/yolov5) [[Notes](paper_notes/yolov5.md)] [yolo, spatial2channel]
- [PP-YOLO: An Effective and Efficient Implementation of Object Detector](https://arxiv.org/abs/2007.12099) [[Notes](paper_notes/pp_yolo.md)] [yolo, paddle-paddle, baidu]
- [PointPainting: Sequential Fusion for 3D Object Detection](https://arxiv.org/pdf/1911.10150.pdf) [[Notes](paper_notes/point_painting.md)] [nuscenece]
- [MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps](https://arxiv.org/abs/2003.06754) [[Notes](paper_notes/motionnet.md)] CVPR 2020 [Unseen moving objects, BEV]
- [Locating Objects Without Bounding Boxes](https://arxiv.org/abs/1806.07564) [[Notes](paper_notes/objects_without_bboxes.md)] CVPR 2019 [weighted Haussdorf distance, NMS-free]
## 2020-11 (18)
- [TSP: Rethinking Transformer-based Set Prediction for Object Detection](https://arxiv.org/abs/2011.10881) [[Notes](paper_notes/tsp.md)] ICCV 2021 [DETR, transformers, Kris Kitani]
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://arxiv.org/abs/2011.12450) [[Notes](paper_notes/sparse_rcnn.md)] CVPR 2020 [DETR, Transformer]
- [Unsupervised Monocular Depth Learning in Dynamic Scenes](https://arxiv.org/abs/2010.16404) [[Notes](paper_notes/learn_depth_and_motion.md)] CoRL 2020 [LearnK improved ver, Google]
- [MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time](https://arxiv.org/abs/2006.16007) [[Notes](paper_notes/monet3d.md)] ICML 2020 [Mono3D, pairwise relationship]
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://arxiv.org/abs/1911.02620) [[Notes](paper_notes/argoverse.md)] CVPR 2019 [HD maps, dataset, CV lidar]
- [The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes](https://arxiv.org/abs/1903.01568) [[Notes](paper_notes/h3d.md)] ICRA 2019
- [Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection](https://arxiv.org/abs/2006.07864) CVPRW 2020 [dataset, Daimler, mono3D]
- [NYC3DCars: A Dataset of 3D Vehicles in Geographic Context](https://www.cs.cornell.edu/~snavely/publications/papers/nyc3dcars_iccv13.pdf) ICCV 2013
- [Towards Fully Autonomous Driving: Systems and Algorithms](https://www.ri.cmu.edu/wp-content/uploads/2017/12/levinson-iv2011.pdf) IV 2011
- [Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding](https://arxiv.org/abs/2005.13423) [[Notes](paper_notes/center3d.md)] [mono3D, LID+DepJoint]
- [ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection](https://arxiv.org/abs/2003.00529) AAAI 2020 oral [mono3D]
- [CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection](https://arxiv.org/abs/2011.04841) [[Notes](paper_notes/centerfusion.md)] WACV 2021 [early fusion, camera, radar]
- [3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation](https://arxiv.org/abs/2011.01535) [[Notes](paper_notes/3d_lanenet+.md)] NeurIPS 2020 workshop [GM Israel, 3D LLD]
- [LSTR: End-to-end Lane Shape Prediction with Transformers](https://arxiv.org/abs/2011.04233) [[Notes](paper_notes/lstr.md)] WACV 2021 [LLD, transformers]
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://arxiv.org/abs/1902.06326) [[Notes](paper_notes/pixor.md)] CVPR 2018 (birds eye view)
- [HDNET/PIXOR++: Exploiting HD Maps for 3D Object Detection](http://proceedings.mlr.press/v87/yang18b/yang18b.pdf) [[Notes](paper_notes/pixor++.md)] CoRL 2018
- [CPNDet: Corner Proposal Network for Anchor-free, Two-stage Object Detection](https://arxiv.org/abs/2007.13816) ECCV 2020 [anchor free, two stage]
- [MVF: End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://arxiv.org/abs/1910.06528) [[Notes](paper_notes/mvf.md)] CoRL 2019 [Waymo, VoxelNet 1st author]
- [Pillar-based Object Detection for Autonomous Driving](https://arxiv.org/abs/2007.10323) [[Notes](paper_notes/pillar_od.md)] ECCV 2020
- [Training-Time-Friendly Network for Real-Time Object Detection](https://arxiv.org/abs/1909.00700) AAAI 2020 [anchor-free, fast training]
- [Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies](https://arxiv.org/abs/2006.06091) [Review of autonomous stack, Yu Huang]
- [Dense Monocular Depth Estimation in Complex Dynamic Scenes](https://openaccess.thecvf.com/content_cvpr_2016/papers/Ranftl_Dense_Monocular_Depth_CVPR_2016_paper.pdf) CVPR 2016
- [Probabilistic Future Prediction for Video Scene Understanding](https://anthonyhu.github.io/research/probabilistic-future-prediction/)
- [AB3D: A Baseline for 3D Multi-Object Tracking](https://arxiv.org/abs/1907.03961) IROS 2020 [3D MOT]
- [Spatial-Temporal Relation Networks for Multi-Object Tracking](https://arxiv.org/abs/1904.11489) ICCV 2019 [MOT, feature location over time]
- [Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking](https://arxiv.org/abs/1802.09298) ICRA 2018 [MOT, IIT, 3D shape]
- [ST-3D: Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking](https://arxiv.org/abs/2004.09305) CVPR 2020 [Peilinag LI, author of VINS and S3DOT]
- [Augment Your Batch: Improving Generalization Through Instance Repetition](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf) CVPR 2020
- [RetinaTrack: Online Single Stage Joint Detection and Tracking](https://arxiv.org/abs/2003.13870) CVPR 2020 [MOT]
- [Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots](https://arxiv.org/abs/1912.12791)
- [Gradient Centralization: A New Optimization Technique for Deep Neural Networks](https://arxiv.org/abs/2004.01461) ECCV 2020 oral
- [Depth Completion via Deep Basis Fitting](https://arxiv.org/abs/1912.10336) WACV 2020
- [BTS: From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://arxiv.org/abs/1907.10326) [monodepth, supervised]
- [The Edge of Depth: Explicit Constraints between Segmentation and Depth](https://arxiv.org/abs/2004.00171) CVPR 2020 [monodepth, Xiaoming Liu]
- [On the Continuity of Rotation Representations in Neural Networks](https://arxiv.org/abs/1812.07035) CVPR 2019 [rotational representation]
- [VDO-SLAM: A Visual Dynamic Object-aware SLAM System](https://arxiv.org/abs/2005.11052) IJRR 2020
- [Dynamic SLAM: The Need For Speed](https://arxiv.org/abs/2002.08584)
- [Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction](https://arxiv.org/abs/2004.10681) ECCV 2020
- [Traffic Light Mapping and Detection](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37259.pdf) [[Notes](paper_notes/tfl_mapping_google.md)] ICRA 2011 [traffic light, Google, Chris Urmson]
- [Traffic light recognition exploiting map and localization at every stage](https://web.yonsei.ac.kr/jksuhr/papers/Traffic%20light%20recognition%20exploiting%20map%20and%20localization%20at%20every%20stage.pdf) [[Notes](paper_notes/tfl_exploting_map_korea.md)] Expert Systems 2017 [traffic light, 鲜于明镐，徐在圭，郑浩奇]
- [Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars](https://arxiv.org/abs/1906.11886) [[Notes](paper_notes/tfl_lidar_map_building_brazil.md)] IJCNN 2019 [traffic light, Espirito Santo Brazil]
## 2020-10 (14)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://arxiv.org/abs/1811.08383) [[Notes](paper_notes/tsm.md)] ICCV 2019 [Song Han, video, object detection]
- [WOD: Waymo Dataset: Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://arxiv.org/abs/1912.04838) [[Notes](paper_notes/wod.md)] CVPR 2020
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) [[Notes](paper_notes/gfocal.md)] NeurIPS 2020 [classification as regression]
- [A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection](https://arxiv.org/abs/2009.13592) NeurIPS 2020 spotlight
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://arxiv.org/abs/2006.07529) NeurIPS 2020
- [RepLoss: Repulsion Loss: Detecting Pedestrians in a Crowd](https://arxiv.org/abs/1711.07752) [[Notes](paper_notes/rep_loss.md)] CVPR 2018 [crowd detection, Megvii]
- [Adaptive NMS: Refining Pedestrian Detection in a Crowd](https://arxiv.org/abs/1904.03629) [[Notes](paper_notes/adaptive_nms.md)] CVPR 2019 oral [crowd detection, NMS]
- [AggLoss: Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd](https://arxiv.org/abs/1807.08407) [[Notes](paper_notes/agg_loss.md)] ECCV 2018 [crowd detection]
- [CrowdDet: Detection in Crowded Scenes: One Proposal, Multiple Predictions](https://arxiv.org/abs/2003.09163) [[Notes](paper_notes/crowd_det.md)] CVPR 2020 oral [crowd detection, Megvii, Earth mover's distance]
- [R2-NMS: NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing](https://arxiv.org/abs/2003.12729) [[Notes](paper_notes/r2_nms.md)] CVPR 2020
- [Double Anchor R-CNN for Human Detection in a Crowd](https://arxiv.org/abs/1909.09998) [[Notes](paper_notes/double_anchor.md)] [head-body bundle]
- [Review: AP vs MR](paper_notes/ap_mr.md)
- [SKU110K: Precise Detection in Densely Packed Scenes](https://arxiv.org/abs/1904.00853) [[Notes](paper_notes/sku110k.md)] CVPR 2019 [crowd detection, no occlusion]
- [GossipNet: Learning non-maximum suppression](https://arxiv.org/abs/1705.02950) CVPR 2017
- [TLL: Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation](https://arxiv.org/abs/1807.01438) ECCV 2018
- [Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels](https://arxiv.org/abs/2010.03506) GCPR 2020 [mono3D, Daniel Cremers, TUM]
- [CubifAE-3D: Monocular Camera Space Cubification on Autonomous Vehicles for Auto-Encoder based 3D Object Detection](https://arxiv.org/abs/2006.04080) [[Notes](paper_notes/cubifae_3d.md)] [mono3D, depth AE pretraining]
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) [[Notes](paper_notes/deformable_detr.md)] ICLR 2021 [Jifeng Dai, DETR]
- [ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) [[Notes](paper_notes/vit.md)] ICLR 2021
- [BYOL: Bootstrap your own latent: A new approach to self-supervised Learning](https://arxiv.org/abs/2006.07733) [self-supervised]
## 2020-09 (15)
- [SDFLabel: Autolabeling 3D Objects With Differentiable Rendering of SDF Shape Priors](https://arxiv.org/abs/1911.11288) [[Notes](paper_notes/sdflabel.md)] CVPR 2020 oral [TRI, differentiable rendering]
- [DensePose: Dense Human Pose Estimation In The Wild](https://arxiv.org/abs/1802.00434) [[Notes](paper_notes/densepose.md)] CVPR 2018 oral [FAIR]
- [NOCS: Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://arxiv.org/abs/1901.02970) CVPR 2019
- [monoDR: Monocular Differentiable Rendering for Self-Supervised 3D Object Detection](https://arxiv.org/abs/2009.14524) [[Notes](paper_notes/monodr.md)] ECCV 2020 [TRI, mono3D]
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://arxiv.org/abs/2008.05711) [[Notes](paper_notes/lift_splat_shoot.md)] ECCV 2020 [BEV-Net, Utoronto, Sanja Fidler]
- [Implicit Latent Variable Model for Scene-Consistent Motion Forecasting](https://arxiv.org/abs/2007.12036) ECCV 2020 [Uber ATG, Rachel Urtasun]
- [FISHING Net: Future Inference of Semantic Heatmaps In Grids](https://arxiv.org/abs/2006.09917) [[Notes](paper_notes/fishing_net.md)] CVPRW 2020 [BEV-Net, Mapping, Zoox]
- [VPN: Cross-view Semantic Segmentation for Sensing Surroundings](https://arxiv.org/abs/1906.03560) [[Notes](paper_notes/vpn.md)] RAL 2020 [Bolei Zhou, BEV-Net]
- [VED: Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks](https://arxiv.org/abs/1804.02176) [[Notes](paper_notes/ved.md)] ICRA 2019 [BEV-Net]
- [Cam2BEV: A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View](https://arxiv.org/abs/2005.04078) [[Notes](paper_notes/cam2bev.md)] ITSC 2020 [BEV-Net]
- [Learning to Look around Objects for Top-View Representations of Outdoor Scenes](https://arxiv.org/abs/1803.10870) [[Notes](paper_notes/learning_to_look_around_objects.md)] ECCV 2018 [BEV-Net, UCSD, Manmohan Chandraker]
- [A Parametric Top-View Representation of Complex Road Scenes](https://arxiv.org/abs/1812.06152) CVPR 2019 [BEV-Net, UCSD, Manmohan Chandraker]
- [FTM: Understanding Road Layout from Videos as a Whole](https://arxiv.org/abs/2007.00822) CVPR 2020 [BEV-Net, UCSD, Manmohan Chandraker]
- [KM3D-Net: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training](https://arxiv.org/abs/2009.00764) [[Notes](paper_notes/km3d_net.md)] RAL 2021 [RTM3D, Peixuan Li]
- [InstanceMotSeg: Real-time Instance Motion Segmentation for Autonomous Driving](https://arxiv.org/abs/2008.07008) [[Notes](paper_notes/instance_mot_seg.md)] IROS 2020 [motion segmentation]
- [MPV-Nets: Monocular Plan View Networks for Autonomous Driving](https://arxiv.org/abs/1905.06937) [[Notes](paper_notes/mpv_nets.md)] IROS 2019 [BEV-Net]
- [Class-Balanced Loss Based on Effective Number of Samples](https://arxiv.org/abs/1901.05555) [[Notes](paper_notes/class_balanced_loss.md)] CVPR 2019 [Focal loss authors]
- [Geometric Pretraining for Monocular Depth Estimation](http://lewissoft.com/pdf/ICRA2020/0035.pdf) [[Notes](paper_notes/geometric_pretraining.md)] ICRA 2020
- [Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving](https://www.mdpi.com/1424-8220/20/4/1181) [[Notes](paper_notes/tfl_robust_japan.md)] Sensors 2020 [traffic light, 金沢]
## 2020-08 (26)
- [Feature-metric Loss for Self-supervised Learning of Depth and Egomotion](https://arxiv.org/abs/2007.10603) [[Notes](paper_notes/feature_metric.md)] ECCV 2020 [feature-metric, local minima, monodepth]
- [Depth-VO-Feat: Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction](https://arxiv.org/abs/1803.03893) CVPR 2018 [feature-metric, monodepth]
- [MonoResMatch: Learning monocular depth estimation infusing traditional stereo knowledge](https://arxiv.org/abs/1904.04144) [[Notes](paper_notes/monoresmatch.md)] CVPR 2019 [monodepth, local minima, cheap stereo GT]
- [SGDepth: Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance](https://arxiv.org/abs/2007.06936) [[Notes](paper_notes/sgdepth.md)] ECCV 2020 [Moving objects]
- [Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding](https://arxiv.org/abs/1806.10556) ECCV 2018 [dynamic objects, rigid and dynamic motion]
- [Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding](https://arxiv.org/abs/1810.06125) TPAMI 2018
- [CC: Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation](https://arxiv.org/abs/1805.09806) [[Notes](paper_notes/cc.md)] CVPR 2019
- [ObjMotionNet: Self-supervised Object Motion and Depth Estimation from Video](https://arxiv.org/abs/1912.04250) [[Notes](paper_notes/obj_motion_net.md)] CVPRW 2020 [object motion prediction, velocity prediction]
- [Instance-wise Depth and Motion Learning from Monocular Videos](https://arxiv.org/abs/1912.09351)
- [Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation](https://arxiv.org/abs/2006.04371)
- [Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues](https://arxiv.org/abs/2006.09876)
- [DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency](https://arxiv.org/abs/1809.01649) ECCV 2018
- [LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments](https://arxiv.org/abs/1807.05696) [mapping]
- [Road-SLAM: Road Marking based SLAM with Lane-level Accuracy](https://www.naverlabs.com/img/autonomousDriving/intelligence/dissertation/Road-SLAM_Road%20Marking%20based%20SLAM%20with%20Lane-level%20Accuracy.pdf) [[Notes](paper_notes/road_slam.md)] [HD mapping]
- [AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot](https://arxiv.org/abs/2007.01813) [[Notes](paper_notes/avp_slam.md)] IROS 2020 [Huawei, HD mapping, Tong Qin, VINS author, autonomous valet parking]
- [AVP-SLAM-Late-Fusion: Mapping and Localization using Semantic Road Marking with Centimeter-level Accuracy in Indoor Parking Lots](https://ieeexplore.ieee.org/abstract/document/8917529) [[Notes](paper_notes/avp_slam_late_fusion.md)] ITSC 2019
- [Lane markings-based relocalization on highway](https://ieeexplore.ieee.org/abstract/document/8917254) ITSC 2019
- [DeepRoadMapper: Extracting Road Topology from Aerial Images](https://openaccess.thecvf.com/content_ICCV_2017/papers/Mattyus_DeepRoadMapper_Extracting_Road_ICCV_2017_paper.pdf) [[Notes](paper_notes/deep_road_mapper.md)] ICCV 2017 [Uber ATG, NOT HD maps]
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://openaccess.thecvf.com/content_cvpr_2018/papers/Bastani_RoadTracer_Automatic_Extraction_CVPR_2018_paper.pdf) CVPR 2018 [NOT HD maps]
- [PolyMapper: Topological Map Extraction From Overhead Images](https://arxiv.org/abs/1812.01497) [[Notes](paper_notes/polymapper.md)] ICCV 2019 [mapping, polygon, NOT HD maps]
- [HRAN: Hierarchical Recurrent Attention Networks for Structured Online Maps](https://openaccess.thecvf.com/content_cvpr_2018/papers/Homayounfar_Hierarchical_Recurrent_Attention_CVPR_2018_paper.pdf) [[Notes](paper_notes/hran.md)] CVPR 2018 [HD mapping, highway, polyline loss, Chamfer distance]
- [Deep Structured Crosswalk: End-to-End Deep Structured Models for Drawing Crosswalks](https://openaccess.thecvf.com/content_ECCV_2018/papers/Justin_Liang_End-to-End_Deep_Structured_ECCV_2018_paper.pdf) [[Notes](paper_notes/deep_structured_crosswalk.md)] ECCV 2018
- [DeepBoundaryExtractor: Convolutional Recurrent Network for Road Boundary Extraction](http://openaccess.thecvf.com/content_CVPR_2019/html/Liang_Convolutional_Recurrent_Network_for_Road_Boundary_Extraction_CVPR_2019_paper.html) [[Notes](paper_notes/deep_boundary_extractor.md)] CVPR 2019 [HD mapping, boundary, polyline loss]
- [DAGMapper: Learning to Map by Discovering Lane Topology](http://openaccess.thecvf.com/content_ICCV_2019/papers/Homayounfar_DAGMapper_Learning_to_Map_by_Discovering_Lane_Topology_ICCV_2019_paper.pdf) [[Notes](paper_notes/dagmapper.md)] ICCV 2019 [HD mapping, highway, forks and merges, polyline loss]
- [Sparse-HD-Maps: Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization](https://arxiv.org/abs/1908.03274) [[Notes](paper_notes/sparse_hd_maps.md)] IROS 2019 oral [Uber ATG, metadata, mapping, localization]
- [Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks](https://arxiv.org/abs/1803.06904) IEEE TGRS 2018
- [Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs](https://www.mdpi.com/1424-8220/20/7/1870/htm) Sensors 2020 [Tsinghua, 3D HD maps]
- [PatchNet: Rethinking Pseudo-LiDAR Representation](https://arxiv.org/abs/2008.04582) [[Notes](paper_notes/patchnet.md)] ECCV 2020 [SenseTime, Wanli Ouyang]
- [D4LCN: Learning Depth-Guided Convolutions for Monocular 3D Object Detection](https://arxiv.org/abs/1912.04799) [[Notes](paper_notes/d4lcn.md)] CVPR 2020 [mono3D]
- [MfS: Learning Stereo from Single Images](https://arxiv.org/abs/2008.01484) [[Notes](paper_notes/mfs.md)] ECCV 2020 [mono for stereo, learn stereo matching with mono]
- [BorderDet: Border Feature for Dense Object Detection](https://arxiv.org/abs/2007.11056) ECCV 2020 oral [Megvii]
- [Scale-Aware Trident Networks for Object Detection](https://arxiv.org/abs/1901.01892) ICCV 2019 [different heads for different scales]
- [Learning Depth from Monocular Videos using Direct Methods](https://arxiv.org/abs/1712.00175)
- [Vid2Depth: Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://arxiv.org/abs/1802.05522) CVPR 2018 [Google]
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://arxiv.org/abs/2008.02268)
- [Supervising the new with the old: learning SFM from SFM](http://openaccess.thecvf.com/content_ECCV_2018/papers/Maria_Klodt_Supervising_the_new_ECCV_2018_paper.pdf) [[Notes](paper_notes/learn_sfm_from_sfm.md)] ECCV 2018
- [Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera](https://arxiv.org/abs/1901.02571) CVPR 2019 [multi-frame monodepth]
- [Don't Forget The Past: Recurrent Depth Estimation from Monocular Video](https://arxiv.org/abs/2001.02613) [multi-frame monodepth, RNN]
- [Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth](https://arxiv.org/abs/1904.07087) [multi-frame monodepth, RNN]
- [Exploiting temporal consistency for real-time video depth estimation](https://arxiv.org/abs/1908.03706) ICCV 2019 [multi-frame monodepth, RNN, indoor]
- [SfM-Net: Learning of Structure and Motion from Video](https://arxiv.org/abs/1704.07804) [dynamic object, SfM]
- [MB-Net: MergeBoxes for Real-Time 3D Vehicles Detection](https://ieeexplore.ieee.org/document/8500395) [[Notes](paper_notes/mb_net.md)] IV 2018 [mono3D: Daimler]
- [BS3D: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images](https://ieeexplore.ieee.org/abstract/document/8814036/) [[Notes](paper_notes/bs3d.md)] IV 2019 [mono3D, Daimler]
- [3D-GCK: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via
Geometrically Constrained Keypoints in Real-Time](https://arxiv.org/abs/2006.13084) [[Notes](paper_notes/3d_gck.md)] IV 2020 [[mono3D, Daimler]
- [UR3D: Distance-Normalized Unified Representation for Monocular 3D Object Detection](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6559_ECCV_2020_paper.php) [[Notes](paper_notes/ur3d.md)] ECCV 2020 [mono3D]
- [DA-3Det: Monocular 3D Object Detection via Feature Domain Adaptation](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540018.pdf) [[Notes](paper_notes/da_3det.md)] ECCV 2020 [mono3D]
- [RAR-Net: Reinforced Axial Refinement Network for Monocular 3D Object Detection](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2822_ECCV_2020_paper.php) [[Notes](paper_notes/rarnet.md)] ECCV 2020 [mono3D]
## 2020-07 (25)
- [CenterTrack: Tracking Objects as Points](https://arxiv.org/abs/2004.01177) [[Notes](paper_notes/centertrack.md)] ECCV 2020 spotlight [camera based 3D MOD, MOT SOTA, CenterNet, video based object detection, Philipp Krähenbühl]
- [CenterPoint: Center-based 3D Object Detection and Tracking](https://arxiv.org/abs/2006.11275) [[Notes](paper_notes/centerpoint.md)] CVPR 2021 [lidar based 3D MOD, CenterNet]
- [Tracktor: Tracking without bells and whistles](https://arxiv.org/abs/1903.05625) [[Notes](paper_notes/tracktor.md)] ICCV 2019 [Tracktor/Tracktor++, Laura Leal-Taixe@TUM]
- [FairMOT: A Simple Baseline for Multi-Object Tracking](https://arxiv.org/abs/2004.01888) [[Notes](paper_notes/fairmot.md)]
- [DeepMOT: A Differentiable Framework for Training Multiple Object Trackers](https://arxiv.org/abs/1906.06618) [[Notes](paper_notes/deepmot.md)] CVPR 2020 [trainable Hungarian, Laura Leal-Taixe@TUM]
- [MPNTracker: Learning a Neural Solver for Multiple Object Tracking](https://arxiv.org/abs/1912.07515) CVPR 2020 oral [trainable Hungarian, Laura Leal-Taixe@TUM]
- [nuScenes: A multimodal dataset for autonomous driving](https://arxiv.org/abs/1903.11027) [[Notes](paper_notes/nuscenes.md)] CVPR 2020 [dataset, point cloud, radar]
- [CBGS: Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection](https://arxiv.org/abs/1908.09492) [[Notes](paper_notes/cbgs.md)] CVPRW 2019 [Megvii, lidar, WAD challenge winner]
- [AFDet: Anchor Free One Stage 3D Object Detection](https://arxiv.org/abs/2006.12671) and [Competition solution](https://arxiv.org/pdf/2006.15505.pdf) [[Notes](paper_notes/afdet.md)] CVPRW 2020 [Horizon robotics, lidar, winning for Waymo challenge]
- Review of MOT and SOT [[Notes](paper_notes/mot_and_sot.md)]
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://arxiv.org/abs/1805.00123) [[Notes](paper_notes/crowdhuman.md)] [megvii, pedestrian, dataset]
- [WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild](https://arxiv.org/abs/1909.12118) [[Notes](paper_notes/widerperson.md)] TMM 2019 [dataset, pedestrian]
- [Tsinghua-Daimler Cyclists: A New Benchmark for Vison-Based Cyclist Detection](http://www.gavrila.net/Publications/iv16_cyclist_benchmark.pdf) [[Notes](paper_notes/tsinghua_daimler_cyclist.md)] IV 2016 [dataset, cyclist Detection]
- [Specialized Cyclist Detection Dataset: Challenging Real-World Computer Vision Dataset for Cyclist Detection Using a Monocular RGB Camera](https://drive.google.com/drive/u/0/folders/1inawrX9NVcchDQZepnBeJY4i9aAI5mg9) [[Notes]([paper_notes/specialized_cyclists.md)] IV 2019 [Extention to KITTI]
- [PointTrack: Segment as Points for Efficient Online Multi-Object Tracking and Segmentation](https://arxiv.org/abs/2007.01550) [[Notes](paper_notes/pointtrack.md)] ECCV 2020 oral [MOTS]
- [PointTrack++ for Effective Online Multi-Object Tracking and Segmentation](https://arxiv.org/abs/2007.01549) [[Notes](paper_notes/pointtrack++.md)] CVPR 2020 workshop [CVPR2020 MOTS Challenge Winner. PointTrack++ ranks first on KITTI MOTS]
- [SpatialEmbedding: Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth](https://arxiv.org/abs/1906.11109) [[Notes](paper_notes/spatial_embedding.md)] ICCV 2019 [one-stage, instance segmentation]
- [BA-Net: Dense Bundle Adjustment Networks](https://arxiv.org/abs/1806.04807) [[Notes](paper_notes/banet.md)] ICLR 2019 [Bundle adjustment, multi-frame monodepth, feature-metric]
- [DeepSFM: Structure From Motion Via Deep Bundle Adjustment](https://arxiv.org/abs/1912.09697) ECCV 2020 oral [multi-frame monodepth, indoor scene]
- [CVD: Consistent Video Depth Estimation](https://arxiv.org/abs/2004.15021) [[Notes](paper_notes/cvd.md)] SIGGRAPH 2020 [multi-frame monodepth, online finetune]
- [DeepV2D: Video to Depth with Differentiable Structure from Motion](https://arxiv.org/abs/1812.04605) [[Notes](paper_notes/deepv2d.md)] ICLR 2020 [multi-frame monodepth, Jia Deng]
- [GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose](https://arxiv.org/abs/1803.02276) [[Notes](paper_notes/geonet.md)] CVPR 2018 [residual optical flow, monodepth, rigid and dynamic motion]
- [GLNet: Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera](https://arxiv.org/abs/1907.05820) [[Notes](paper_notes/glnet.md)] ICCV 2019 [online finetune, rigid and dynamic motion]
- [Depth Hints: Self-Supervised Monocular Depth Hints](https://arxiv.org/abs/1909.09051) [[Notes](paper_notes/depth_hints.md)] ICCV 2019 [monodepth, local minima, cheap stereo GT]
- [MonoUncertainty: On the uncertainty of self-supervised monocular depth estimation](https://arxiv.org/abs/2005.06209) [[Notes](paper_notes/mono_uncertainty.md)] CVPR 2020 [depth uncertainty]
- [Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment](https://arxiv.org/abs/1909.13163) [[Notes](paper_notes/ba_sfm_learner.md)] [Bundle adjustment, xmotors.ai, multi-frame monodepth]
- [Kinematic 3D Object Detection in Monocular Video](https://arxiv.org/abs/2007.09548) [[Notes](paper_notes/kinematic_mono3d.md)] ECCV 2020 [multi-frame mono3D, Xiaoming Liu]
- [VelocityNet: Camera-based vehicle velocity estimation from monocular video](https://arxiv.org/abs/1802.07094) [[Notes](paper_notes/velocity_net.md)] CVPR 2017 workshop [monocular velocity estimation, CVPR 2017 challenge winner]
- [Vehicle Centric VelocityNet: End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera](https://arxiv.org/abs/2006.04082) [[Notes](paper_notes/vehicle_centric_velocity_net.md)] [monocular velocity estimation, monocular distance, SOTA]
## 2020-06 (20)
- [LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain](http://personal.stevens.edu/~benglot/Shan_Englot_IROS_2018_Preprint.pdf) [[Notes](paper_notes/lego_loam.md)] IROS 2018 [lidar, mapping]
- [PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction](http://openaccess.thecvf.com/content_ICCV_2019/papers/Rasouli_PIE_A_Large-Scale_Dataset_and_Models_for_Pedestrian_Intention_Estimation_ICCV_2019_paper.pdf) [[Notes](paper_notes/pie.md)] ICCV 2019
- [JAAD: Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian
Crosswalk Behavior](http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w3/Rasouli_Are_They_Going_ICCV_2017_paper.pdf) ICCV 2017
- [Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs](https://bmvc2019.org/wp-content/uploads/papers/0283-paper.pdf) BMVC 2019
- [Is the Pedestrian going to Cross? Answering by 2D Pose Estimation](https://arxiv.org/abs/1807.10580) IV 2018
- [Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation](https://arxiv.org/abs/1910.03858) ITSC 2019 [skeleton, pedestrian, cyclist intention]
- [Attentive Single-Tasking of Multiple Tasks](https://arxiv.org/abs/1904.08918) CVPR 2019
- [DETR: End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) [[Notes](paper_notes/detr.md)] ECCV 2020 oral [FAIR]
- [Transformer: Attention Is All You Need](https://arxiv.org/abs/1706.03762) [[Notes](paper_notes/transformer.md)] NIPS 2017
- [SpeedNet: Learning the Speediness in Videos](https://arxiv.org/abs/2004.06130) [[Notes](paper_notes/speednet.md)] CVPR 2020 oral
- [MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships](https://arxiv.org/abs/2003.00504) [[Notes](paper_notes/monopair.md)] CVPR 2020 [Mono3D, pairwise relationship]
- [SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation](https://arxiv.org/abs/2002.10111) [[Notes](paper_notes/smoke.md)] CVPRW 2020 [Mono3D, Zongmu]
- [Vehicle Re-ID for Surround-view Camera System](https://drive.google.com/file/d/1e6y8wtHAricaEHS9CpasSGOx0aAxCGib/view) [[Notes](paper_notes/reid_surround_fisheye.md)] CVPRW 2020 [tireline, vehicle ReID, Zongmu]
- [End-to-End Lane Marker Detection via Row-wise Classification](https://arxiv.org/abs/2005.08630) [[Notes](paper_notes/e2e_lmd.md)] [Qualcomm Korea, LLD as cls]
- [Reliable multilane detection and classification by utilizing CNN as a regression network](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Chougule_Reliable_multilane_detection_and_classification_by_utilizing_CNN_as_a_ECCVW_2018_paper.pdf) ECCV 2018 [LLD as reg]
- [SUPER: A Novel Lane Detection System](https://arxiv.org/abs/2005.07277) [[Notes](paper_notes/super.md)]
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://arxiv.org/abs/1908.00821) ICCV 2019
- [StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation](http://www.bmva.org/bmvc/2015/papers/paper109/paper109.pdf) BMVC 2015
- [StixelNetV2: Real-time category-based and general obstacle detection for autonomous driving](http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w3/Garnett_Real-Time_Category-Based_and_ICCV_2017_paper.pdf) [[Notes](paper_notes/stixelnetv2.md)] ICCV 2017 [DS]
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158) [[Notes](paper_notes/subpixel_conv.md)] CVPR 2016 [channel-to-pixel]
- [Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints](https://arxiv.org/abs/1912.04363) [mono3D]
- [Self-Mono-SF: Self-Supervised Monocular Scene Flow Estimation](https://arxiv.org/abs/2004.04143) [[Notes](paper_notes/self_mono_sf.md)] CVPR 2020 oral [scene-flow, Stereo input]
- [MEBOW: Monocular Estimation of Body Orientation In the Wild](https://arxiv.org/abs/2011.13688) [[Notes](paper_notes/mebow.md)] CVPR 2020
- [VG-NMS: Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes](https://arxiv.org/abs/2006.08547) [[Notes](paper_notes/vg_nms.md)] NeurIPS 2019 workshop [Crowded scene, NMS, Daimler]
- [WYSIWYG: What You See is What You Get: Exploiting Visibility for 3D Object Detection](https://arxiv.org/abs/1912.04986) [[Notes](paper_notes/wysiwyg.md)] CVPR 2020 oral [occupancy grid]
- [Real-Time Panoptic Segmentation From Dense Detections](https://arxiv.org/abs/1912.01202) [[Notes](paper_notes/realtime_panoptic.md)] CVPR 2020 oral [bbox + semantic segmentation = panoptic segmentation, Toyota]
- [Human-Centric Efficiency Improvements in Image Annotation for Autonomous Driving](https://drive.google.com/file/d/1DY95vfWBLKOOZZyq8gLDd0heZ6aBSdji/view) [[Notes](paper_notes/human_centric_annotation.md)] CVPRW 2020 [efficient annotation]
- [SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving](https://arxiv.org/abs/2005.03844) [[Notes](paper_notes/surfel_gan.md)] CVPR 2020 oral [Waymo, auto data generation, surfel]
- [LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World](https://arxiv.org/abs/2006.09348) [[Notes](paper_notes/lidarsim.md)] CVPR 2020 oral [Uber ATG, auto data generation, surfel]
- [SuMa++: Efficient LiDAR-based Semantic SLAM](http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chen2019iros.pdf) IROS 2019 [semantic segmentation, lidar, SLAM]
- [PON/PyrOccNet: Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks](https://arxiv.org/abs/2003.13402) [[Notes](paper_notes/pyroccnet.md)] CVPR 2020 oral [BEV-Net, OFT]
- [MonoLayout: Amodal scene layout from a single image](https://arxiv.org/abs/2002.08394) [[Notes](paper_notes/monolayout.md)] WACV 2020 [BEV-Net]
- [BEV-Seg: Bird’s Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud](https://arxiv.org/abs/2006.11436) [[Notes](paper_notes/bev_seg.md)] CVPR 2020 workshop [BEV-Net, Mapping]
- [A Geometric Approach to Obtain a Bird's Eye View from an Image](https://arxiv.org/abs/1905.02231) ICCVW 2019 [mapping, geometry, Andrew Zisserman]
- [FrozenDepth: Learning the Depths of Moving People by Watching Frozen People](https://arxiv.org/abs/1904.11111) [[Notes](paper_notes/frozen_depth.md)] CVPR 2019 oral
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://arxiv.org/abs/1502.00956) TRO 2015
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://arxiv.org/abs/1610.06475) TRO 2016
- [CubeSLAM: Monocular 3D Object SLAM](https://arxiv.org/abs/1806.00557) [[Notes](paper_notes/cube_slam.md)] TRO 2019 [dynamic SLAM, orb slam + mono3D]
- [ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings](https://arxiv.org/abs/2003.12980) [[Notes](paper_notes/cluster_vo.md)] CVPR 2020 [general dynamic SLAM]
- [S3DOT: Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving](https://arxiv.org/abs/1807.02062) [[Notes](paper_notes/s3dot.md)] ECCV 2018 [Peiliang Li]
- [Multi-object Monocular SLAM for Dynamic Environments](https://arxiv.org/abs/2002.03528) [[Notes](paper_notes/multi_object_mono_slam.md)] IV 2020 [monolayout authors]
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://arxiv.org/abs/1709.02371) [[Notes](paper_notes/pwc_net.md)] CVPR 2018 oral [Optical flow]
- [LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation](https://arxiv.org/abs/1805.07036) CVPR 2018 [Optical flow]
- [FlowNet: Learning Optical Flow With Convolutional Networks](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dosovitskiy_FlowNet_Learning_Optical_ICCV_2015_paper.pdf) ICCV 2015 [Optical flow]
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://arxiv.org/abs/1612.01925) CVPR 2017 [Optical flow]
- [ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network](https://arxiv.org/abs/1811.11431) CVPR 2019 [semantic segmentation, lightweight]
- [Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes](https://arxiv.org/abs/1908.06316) ICCV 2019 [depth uncertainty]
## 2020-05 (19)
- [Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems](https://arxiv.org/abs/1809.07408) [[Notes](paper_notes/hevi.md)] [Honda] ICRA 2019
- [PackNet: 3D Packing for Self-Supervised Monocular Depth Estimation](https://arxiv.org/abs/1905.02693) [[Notes](paper_notes/packnet.md)] CVPR 2020 oral [Scale aware depth]
- [PackNet-SG: Semantically-Guided Representation Learning for Self-Supervised Monocular Depth](https://arxiv.org/abs/2002.12319) [[Notes](paper_notes/packnet_sg.md)] ICLR 2020 [TRI, infinite-depth problem]
- [TrianFlow: Towards Better Generalization: Joint Depth-Pose Learning without PoseNet](https://arxiv.org/abs/2004.01314) [[Notes](paper_notes/trianflow.md)] CVPR 2020 [Scale aware]
- [Understanding the Limitations of CNN-based Absolute Camera Pose Regression](https://arxiv.org/abs/1903.07504) [[Notes](paper_notes/understanding_apr.md)] CVPR 2019 [Drawbacks of PoseNet, MapNet, Laura Leal-Taixe@TUM]
- [To Learn or Not to Learn: Visual Localization from Essential Matrices](https://arxiv.org/abs/1908.01293) [[Notes](paper_notes/to_learn_or_not.md)] ICRA 2020 [SIFT + 5 pt solver >> others for VO, Laura Leal-Taixe@TUM]
- [DF-VO: Visual Odometry Revisited: What Should Be Learnt?](https://arxiv.org/abs/1909.09803) [[Notes](paper_notes/df_vo.md)] ICRA 2020 [Depth and Flow for accurate VO]
- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://arxiv.org/abs/2003.01060) [[Notes](paper_notes/d3vo.md)] CVPR 2020 oral [Daniel Cremers, TUM, depth uncertainty]
- [Network Slimming: Learning Efficient Convolutional Networks through Network Slimming](https://arxiv.org/abs/1708.06519) [[Notes](paper_notes/network_slimming.md)] ICCV 2017
- [BatchNorm Pruning: Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers](https://arxiv.org/abs/1802.00124) [[Notes](paper_notes/batchnorm_pruning.md)] ICLR 2018
- [Direct Sparse Odometry](https://arxiv.org/abs/1607.02565) PAMI 2018
- [Train in Germany, Test in The USA: Making 3D Object Detectors Generalize](https://arxiv.org/abs/2005.08139) [[Notes](paper_notes/train_in_germany.md)] CVPR 2020
- [PseudoLidarV3: End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection](https://arxiv.org/abs/2004.03080) [[Notes](paper_notes/pseudo_lidar_v3.md)] CVPR 2020
- [ATSS: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://arxiv.org/abs/1912.02424) [[Notes](paper_notes/atss.md)] CVPR 2020 oral
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://arxiv.org/abs/1911.08287) AAAI 2020
- [Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation](https://arxiv.org/abs/2005.03572) [Journal version]
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://arxiv.org/abs/2004.10934) [[Notes](paper_notes/yolov4.md)]
- [CBN: Cross-Iteration Batch Normalization](https://arxiv.org/abs/2002.05712) [[Notes](paper_notes/cbn.md)]
- [Stitcher: Feedback-driven Data Provider for Object Detection](https://arxiv.org/abs/2004.12432) [[Notes](paper_notes/stitcher.md)]
- [SKNet: Selective Kernel Networks](https://arxiv.org/abs/1903.06586) [[Notes](paper_notes/sknet.md)] CVPR 2019
- [CBAM: Convolutional Block Attention Module](https://arxiv.org/abs/1807.06521) [[Notes](paper_notes/cbam.md)] ECCV 2018
- [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955) [[Notes](paper_notes/resnest.md)]
## 2020-04 (14)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://arxiv.org/pdf/1812.03079.pdf) [[Notes](paper_notes/chauffeurnet.md)] RSS 2019 [Waymo]
- [IntentNet: Learning to Predict Intention from Raw Sensor Data](http://www.cs.toronto.edu/~wenjie/papers/intentnet_corl18.pdf) [[Notes](paper_notes/intentnet.md)] CoRL 2018 [Uber ATG, perception and prediction, Lidar+Map]
- [RoR: Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions](https://arxiv.org/abs/1906.08945) [[Notes](paper_notes/ror.md)] CVPR 2019 [Zoox]
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://arxiv.org/abs/1910.05449) [[Notes](paper_notes/multipath.md)] CoRL 2019 [Waymo, authors from RoR and ChauffeurNet]
- [NMP: End-to-end Interpretable Neural Motion Planner](http://www.cs.toronto.edu/~wenjie/papers/cvpr19/nmp.pdf) [[Notes](paper_notes/nmp.md)] CVPR 2019 oral [Uber ATG]
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://arxiv.org/abs/1809.10732) [[Notes](paper_notes/multipath_uber.md)] ICRA 2019 [Henggang Cui, Multimodal, Uber ATG Pittsburgh]
- [Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving](https://arxiv.org/abs/1808.05819) WACV 2020 [Uber ATG Pittsburgh]
- [TensorMask: A Foundation for Dense Object Segmentation](https://arxiv.org/abs/1903.12174) [[Notes](paper_notes/tensormask.md)] ICCV 2019 [single-stage instance seg]
- [BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation](https://arxiv.org/abs/2001.00309) [[Notes](paper_notes/blendmask.md)] CVPR 2020 oral
- [Mask Encoding for Single Shot Instance Segmentation](https://arxiv.org/abs/2003.11712) [[Notes](paper_notes/meinst.md)] CVPR 2020 oral [single-stage instance seg, Chunhua Shen]
- [PolarMask: Single Shot Instance Segmentation with Polar Representation](https://arxiv.org/abs/1909.13226) [[Notes](paper_notes/polarmask.md)] CVPR 2020 oral [single-stage instance seg]
- [SOLO: Segmenting Objects by Locations](https://arxiv.org/abs/1912.04488) [[Notes](paper_notes/solo.md)] ECCV 2020 [single-stage instance seg, Chunhua Shen]
- [SOLOv2: Dynamic, Faster and Stronger](https://arxiv.org/abs/2003.10152) [[Notes](paper_notes/solov2.md)] [single-stage instance seg, Chunhua Shen]
- [CondInst: Conditional Convolutions for Instance Segmentation](https://arxiv.org/abs/2003.05664) [[Notes](paper_notes/condinst.md)] ECCV 2020 oral [single-stage instance seg, Chunhua Shen]
- [CenterMask: Single Shot Instance Segmentation With Point Representation](https://arxiv.org/abs/2004.04446) [[Notes](paper_notes/centermask.md)]CVPR 2020
## 2020-03 (15)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://arxiv.org/abs/1710.06288) [[Notes](paper_notes/vpgnet.md)] ICCV 2017
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://arxiv.org/abs/1905.07553) [[Notes](paper_notes/task_grouping.md)] [Stanford, MTL] ICML 2020
- [MGDA: Multi-Task Learning as Multi-Objective Optimization](https://arxiv.org/abs/1810.04650) NeurIPS 2018
- [Taskonomy: Disentangling Task Transfer Learning](https://arxiv.org/abs/1804.08328) [[Notes](paper_notes/taskonomy.md)] CVPR 2018
- [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883) [[Notes](paper_notes/rethinking_pretraining.md)] ICCV 2019 [Kaiming He]
- [UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor](https://arxiv.org/abs/1907.04011) [[Notes](paper_notes/unsuperpoint.md)] [superpoint]
- [KP2D: Neural Outlier Rejection for Self-Supervised Keypoint Learning](https://arxiv.org/abs/1912.10615) [[Notes](paper_notes/kp2d.md)] ICLR 2020 (pointNet)
- [KP3D: Self-Supervised 3D Keypoint Learning for Ego-motion Estimation](https://arxiv.org/abs/1912.03426) [[Notes](paper_notes/kp3d.md)] CoRL 2020 [Toyota, superpoint]
- [NG-RANSAC: Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses](https://arxiv.org/abs/1905.04132) [[Notes](paper_notes/ng_ransac.md)] ICCV 2019 [pointNet]
- [Learning to Find Good Correspondences](https://arxiv.org/abs/1711.05971) [[Notes](paper_notes/learning_correspondence.md)] CVPR 2018 Oral (pointNet)
- [RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving](https://arxiv.org/abs/1911.09712) [[Notes](paper_notes/refined_mpl.md)] [Huawei, Mono3D]
- [DSP: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation](https://arxiv.org/abs/2002.01619) [[Notes](paper_notes/dsp.md)] AAAI 2020 (SenseTime, Mono3D)
- [Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks](https://arxiv.org/abs/1903.02193) (LLD, LSTM)
- [LaneNet: Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://arxiv.org/abs/1802.05591) [[Notes](paper_notes/lanenet.md)] IV 2018 (LaneNet)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](http://openaccess.thecvf.com/content_ICCV_2019/papers/Garnett_3D-LaneNet_End-to-End_3D_Multiple_Lane_Detection_ICCV_2019_paper.pdf) [[Notes](paper_notes/3d_lanenet.md)] ICCV 2019
- [Semi-Local 3D Lane Detection and Uncertainty Estimation](https://arxiv.org/abs/2003.05257) [[Notes](paper_notes/semilocal_3d_lanenet.md)] [GM Israel, 3D LLD]
- [Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection](https://arxiv.org/abs/2003.10656) [[Notes](paper_notes/gen_lanenet.md)] ECCV 2020 [Apollo, 3D LLD]
- [Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty](https://arxiv.org/abs/1711.09026) CVPR 2018 [Egocentric prediction]
- [It’s Not All About Size: On the Role of Data Properties in Pedestrian Detection](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11129/Rasouli_Its_Not_All_About_Size_On_the_Role_of_Data_ECCVW_2018_paper.pdf) ECCV 2018 [pedestrian]
## 2020-02 (12)
- [Associative Embedding: End-to-End Learning for Joint Detection and Grouping](https://arxiv.org/abs/1611.05424) [[Notes](paper_notes/associative_embedding.md)] NIPS 2017
- [Pixels to Graphs by Associative Embedding](https://arxiv.org/abs/1706.07365) [[Notes](paper_notes/pixels_to_graphs.md)] NIPS 2017
- [Social LSTM: Human Trajectory Prediction in Crowded Spaces](http://cvgl.stanford.edu/papers/CVPR16_Social_LSTM.pdf) [[Notes](paper_notes/social_lstm.md)] CVPR 2017
- [Online Video Object Detection using Association LSTM](http://openaccess.thecvf.com/content_ICCV_2017/papers/Lu__Online_Video_ICCV_2017_paper.pdf) [[Notes](paper_notes/association_lstm.md)] [single stage, recurrent]
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) [[Notes](paper_notes/superpoint.md)] CVPR 2018 (channel-to-pixel, deep SLAM, Magic Leap)
- [PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193) [[Notes](paper_notes/pointrend.md)] CVPR 2020 Oral [Kaiming He, FAIR]
- [Multigrid: A Multigrid Method for Efficiently Training Video Models](https://arxiv.org/abs/1912.00998) [[Notes](paper_notes/multigrid_training.md)] CVPR 2020 Oral [Kaiming He, FAIR]
- [GhostNet: More Features from Cheap Operations](https://arxiv.org/abs/1911.11907) [[Notes](paper_notes/ghostnet.md)] CVPR 2020
- [FixRes: Fixing the train-test resolution discrepancy](https://arxiv.org/abs/1906.06423) [[Notes](paper_notes/fixres.md)] NIPS 2019 [FAIR]
- [MoVi-3D: Towards Generalization Across Depth for Monocular 3D Object Detection](https://arxiv.org/abs/1912.08035) [[Notes](paper_notes/movi_3d.md)] ECCV 2020 [Virtual Cam, viewport, Mapillary/Facebook, Mono3D]
- [Amodal Completion and Size Constancy in Natural Scenes](https://arxiv.org/abs/1509.08147) [[Notes](paper_notes/amodal_completion.md)] ICCV 2015 (Amodal completion)
- [MoCo: Momentum Contrast for Unsupervised Visual Representation Learning](https://arxiv.org/abs/1911.05722) [[Notes](paper_notes/moco.md)] CVPR 2020 Oral [FAIR, Kaiming He]
## 2020-01 (19)
- [Double Descent: Reconciling modern machine learning practice and the bias-variance trade-of](https://arxiv.org/abs/1812.11118) [[Notes](paper_notes/double_descent.md)] PNAS 2019
- [Deep Double Descent: Where Bigger Models and More Data Hurt](https://arxiv.org/abs/1912.02292) [[Notes](paper_notes/deep_double_descent.md)]
- [Visualizing the Loss Landscape of Neural Nets](https://arxiv.org/abs/1712.09913) NIPS 2018
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://arxiv.org/pdf/1803.06184.pdf) CVPR 2018 (dataset)
- [ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving](https://arxiv.org/abs/1811.12222) [[Notes](paper_notes/apollocar3d.md)] CVPR 2019
- [Part-level Car Parsing and Reconstruction from a Single Street View](https://arxiv.org/abs/1811.10837) [[Notes](paper_notes/apollo_car_parts.md)] [Baidu]
- [6D-VNet: End-to-end 6DoF Vehicle Pose Estimation from Monocular RGB Images](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Autonomous%20Driving/Wu_6D-VNet_End-to-End_6-DoF_Vehicle_Pose_Estimation_From_Monocular_RGB_Images_CVPRW_2019_paper.pdf) [[Notes](paper_notes/6d_vnet.md)] CVPR 2019
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://arxiv.org/abs/2001.03343) [[Notes](paper_notes/rtm3d.md)] ECCV 2020 spotlight
- [DORN: Deep Ordinal Regression Network for Monocular Depth Estimation](https://arxiv.org/abs/1806.02446) [[Notes](paper_notes/dorn.md)] CVPR 2018 [monodepth, supervised]
- [D&T: Detect to Track and Track to Detect](https://arxiv.org/abs/1710.03958) [[Notes](paper_notes/detect_track.md)] ICCV 2017 (from Feichtenhofer)
- [CRF-Net: A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://ieeexplore.ieee.org/abstract/document/8916629/) [[Notes](paper_notes/crf_net.md)] SDF 2019 (radar detection)
- [RVNet: Deep Sensor Fusion of Monocular Camera and Radar for Image-based Obstacle Detection in Challenging Environments](https://www.researchgate.net/profile/Vijay_John3/publication/335833918_RVNet_Deep_Sensor_Fusion_of_Monocular_Camera_and_Radar_for_Image-based_Obstacle_Detection_in_Challenging_Environments/links/5d7f164e92851c87c38b09f1/RVNet-Deep-Sensor-Fusion-of-Monocular-Camera-and-Radar-for-Image-based-Obstacle-Detection-in-Challenging-Environments.pdf) [[Notes](paper_notes/rvnet.md)] PSIVT 2019
- [RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles](https://arxiv.org/abs/1905.00526) [[Notes](paper_notes/rrpn_radar.md)] ICIP 2019
- [ROLO: Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking](https://arxiv.org/abs/1607.05781) [[Notes](paper_notes/rolo.md)] ISCAS 2016
- [Recurrent SSD: Recurrent Multi-frame Single Shot Detector for Video Object Detection](https://www.merl.com/publications/docs/TR2018-137.pdf) [[Notes](paper_notes/recurrent_ssd.md)] BMVC 2018 (Mitsubishi)
- [Recurrent RetinaNet: A Video Object Detection Model Based on Focal Loss](https://doi.org/10.1007/978-3-030-04212-7_44) [[Notes](paper_notes/recurrent_retinanet.md)] ICONIP 2018 (single stage, recurrent)
- [Actions as Moving Points](https://arxiv.org/abs/2001.04608) [[Notes](paper_notes/moc.md)] [not suitable for online]
- [The PREVENTION dataset: a novel benchmark for PREdiction of VEhicles iNTentIONs](10.1109/ITSC.2019.8917433) [[Notes](paper_notes/prevention_dataset.md)] ITSC 2019 [dataset, cut-in]
- [Semi-Automatic High-Accuracy Labelling Tool for Multi-Modal Long-Range Sensor Dataset](https://sci-hub.tw/10.1109/IVS.2018.8500672) [[Notes](paper_notes/prevention_annotation.md)] IV 2018
- [Astyx dataset: Automotive Radar Dataset for Deep Learning Based 3D Object Detection](https://www.astyx.com/fileadmin/redakteur/dokumente/Automotive_Radar_Dataset_for_Deep_learning_Based_3D_Object_Detection.PDF) [[Notes](paper_notes/astyx_dataset.md)] EuRAD 2019 (Astyx)
- [Astyx camera radar: Deep Learning Based 3D Object Detection for Automotive Radar and Camera](https://www.astyx.net/fileadmin/redakteur/dokumente/Deep_Learning_Based_3D_Object_Detection_for_Automotive_Radar_and_Camera.PDF) [[Notes](paper_notes/astyx_radar_camera_fusion.md)] EuRAD 2019 (Astyx)
## 2019-12 (12)
- [How Do Neural Networks See Depth in Single Images?](http://openaccess.thecvf.com/content_ICCV_2019/papers/van_Dijk_How_Do_Neural_Networks_See_Depth_in_Single_Images_ICCV_2019_paper.pdf) [[Notes](paper_notes/what_monodepth_see.md)] ICCV 2019
- [Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera](https://arxiv.org/abs/1807.00275) ICRA 2019 (depth completion)
- [DC: Depth Coefficients for Depth Completion](https://arxiv.org/abs/1903.05421) [[Notes](paper_notes/depth_coeff.md)] CVPR 2019 [Xiaoming Liu, Multimodal]
- [Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation](https://arxiv.org/pdf/1611.02174.pdf) [[Notes](paper_notes/depth_from_one_line.md)] ICRA 2017
- [VO-Monodepth: Enhancing self-supervised monocular depth estimation with traditional visual odometry](https://arxiv.org/abs/1908.03127) [[Notes](paper_notes/vo_monodepth.md)] 3DV 2019 (sparse to dense)
- [Probabilistic Object Detection: Definition and Evaluation](https://arxiv.org/abs/1811.10800) [[Notes](paper_notes/pdq.md)]
- [The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation](https://arxiv.org/abs/1904.03215) [[Notes](paper_notes/fishyscape.md)] ICCV 2019
- [On Calibration of Modern Neural Networks](https://arxiv.org/abs/1706.04599) [[Notes](paper_notes/calib_modern_nn.md)] ICML 2017 (Weinberger)
- [Extreme clicking for efficient object annotation](https://arxiv.org/abs/1708.02750) [[Notes](paper_notes/extreme_clicking.md)] ICCV 2017
- [Radar and Camera Early Fusion for Vehicle Detection in Advanced Driver Assistance Systems](https://ml4ad.github.io/files/papers/Radar%20and%20Camera%20Early%20Fusion%20for%20Vehicle%20Detection%20in%20Advanced%20Driver%20Assistance%20Systems.pdf) [[Notes](paper_notes/radar_camera_qcom.md)] NeurIPS 2019 (radar)
- [Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector](https://arxiv.org/abs/1901.10609) [[Notes](paper_notes/deep_active_learning_lidar.md)] IV 2019
- [C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion](https://arxiv.org/abs/1909.02533) [[Notes](paper_notes/c3dpo.md)] ICCV 2019
- [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) [[Notes](paper_notes/yolact.md)] ICCV 2019 [single-stage instance seg]
- [YOLACT++: Better Real-time Instance Segmentation](https://arxiv.org/abs/1912.06218) [single-stage instance seg]
## 2019-11 (20)
- [Review of Image and Feature Descriptors](paper_notes/review_descriptors.md)
- [Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors](http://openaccess.thecvf.com/content_ICCVW_2019/papers/CVRSUAD/Major_Vehicle_Detection_With_Automotive_Radar_Using_Deep_Learning_on_Range-Azimuth-Doppler_ICCVW_2019_paper.pdf) [[Notes](paper_notes/radar_fft_qcom.md)] ICCV 2019
- [GPP: Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road](https://arxiv.org/abs/1811.06666) [[Notes](paper_notes/gpp.md)] IV 2020 [UCSD, Trevidi, mono 3DOD]
- [MVRA: Multi-View Reprojection Architecture for Orientation Estimation](http://openaccess.thecvf.com/content_ICCVW_2019/papers/ADW/Choi_Multi-View_Reprojection_Architecture_for_Orientation_Estimation_ICCVW_2019_paper.pdf) [[Notes](paper_notes/mvra.md)] ICCV 2019
- [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf)
- [Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving](https://arxiv.org/abs/1904.04620) [[Notes](paper_notes/gaussian_yolov3.md)] ICCV 2019 (Detection with Uncertainty)
- [Bayesian YOLOv3: Uncertainty Estimation in One-Stage Object Detection](https://arxiv.org/abs/1905.10296) [[Notes](paper_notes/bayesian_yolov3.md)] [DriveU]
- [Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection](https://arxiv.org/abs/1804.05132) [[Notes](paper_notes/towards_safe_ad.md)] ITSC 2018 (DriveU)
- [Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection](https://arxiv.org/abs/1809.05590) [[Notes](paper_notes/towards_safe_ad2.md)] IV 2019 (DriveU)
- [Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving](https://arxiv.org/abs/1909.12358) [[Notes](paper_notes/towards_safe_ad_calib.md)] IROS 2019 (DriveU)
- [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](https://arxiv.org/abs/1903.08701) [[Notes](paper_notes/lasernet.md)] CVPR 2019 (uncertainty)
- [LaserNet KL: Learning an Uncertainty-Aware Object Detector for Autonomous Driving](https://arxiv.org/abs/1910.11375) [[Notes](paper_notes/lasernet_kl.md)] [LaserNet with KL divergence]
- [IoUNet: Acquisition of Localization Confidence for Accurate Object Detection](https://arxiv.org/abs/1807.11590) [[Notes](paper_notes/iou_net.md)] ECCV 2018
- [gIoU: Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://arxiv.org/abs/1902.09630) [[Notes](paper_notes/giou.md)] CVPR 2019
- [The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks](https://arxiv.org/abs/1705.08790) CVPR 2018 [IoU as loss]
- [KL Loss: Bounding Box Regression with Uncertainty for Accurate Object Detection](https://arxiv.org/abs/1809.08545) [[Notes](paper_notes/kl_loss.md)] CVPR 2019
- [CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth](https://arxiv.org/abs/1904.02028) [[Notes](paper_notes/cam_conv.md)] CVPR 2019
- [BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors](https://arxiv.org/abs/1903.03838) [[Notes](paper_notes/bayes_od.md)]
- [TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching](https://arxiv.org/abs/1906.04463) [[Notes](paper_notes/twsm_net.md)] ICIP 2019
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://arxiv.org/abs/1807.00263) [[Notes](paper_notes/dl_regression_calib.md)] ICML 2018
- [Calibrating Uncertainties in Object Localization Task](https://arxiv.org/abs/1811.11210) [[Notes](paper_notes/2dod_calib.md)] NIPS 2018
- [SMWA: On the Over-Smoothing Problem of CNN Based Disparity Estimation](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_On_the_Over-Smoothing_Problem_of_CNN_Based_Disparity_Estimation_ICCV_2019_paper.pdf) [[Notes](paper_notes/smwa.md)] ICCV 2019 [Multimodal, depth estimation]
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://arxiv.org/abs/1709.07492) [[Notes](paper_notes/sparse_to_dense.md)] ICRA 2018 (depth completion)
## 2019-10 (18)
- [Review of monocular object detection](paper_notes/review_mono_3dod.md)
- [Review of 2D 3D contraints in Mono 3DOD](paper_notes/mono_3dod_2d3d_constraints.md)
- [MonoGRNet 2: Monocular 3D Object Detection via Geometric Reasoning on Keypoints](https://arxiv.org/abs/1905.05618) [[Notes](paper_notes/monogrnet_russian.md)] [estimates depth from keypoints]
- [Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image](https://arxiv.org/abs/1703.07570) [[Notes](paper_notes/deep_manta.md)] CVPR 2017
- [SS3D: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss](https://arxiv.org/abs/1906.08070) [[Notes](paper_notes/ss3d.md)] [rergess distance from images, centernet like]
- [GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving](https://arxiv.org/abs/1903.10955) [[Notes](paper_notes/gs3d.md)] CVPR 2019
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://arxiv.org/abs/1907.06038) [[Notes](paper_notes/m3d_rpn.md)] ICCV 2019 oral [3D anchors, cyclists, Xiaoming Liu]
- [TLNet: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection](https://arxiv.org/abs/1906.01193) [[Notes](paper_notes/tlnet.md)] CVPR 2019
- [A Survey on 3D Object Detection Methods for Autonomous Driving Applications](http://wrap.warwick.ac.uk/114314/1/WRAP-survey-3D-object-detection-methods-autonomous-driving-applications-Arnold-2019.pdf) [[Notes](paper_notes/3dod_review.md)] TITS 2019 [Review]
- [BEV-IPM: Deep Learning based Vehicle Position and Orientation Estimation via Inverse Perspective Mapping Image](https://ieeexplore.ieee.org/abstract/document/8814050) [[Notes](paper_notes/bev_od_ipm.md)] IV 2019
- [ForeSeE: Task-Aware Monocular Depth Estimation for 3D Object Detection](https://arxiv.org/abs/1909.07701) [[Notes](paper_notes/foresee_mono3dod.md)] AAAI 2020 oral [successor to pseudo-lidar, mono 3DOD SOTA]
- [Obj-dist: Learning Object-specific Distance from a Monocular Image](https://arxiv.org/abs/1909.04182) [[Notes](paper_notes/obj_dist_iccv2019.md)] ICCV 2019 (xmotors.ai + NYU) [monocular distance]
- [DisNet: A novel method for distance estimation from monocular camera](https://project.inria.fr/ppniv18/files/2018/10/paper22.pdf) [[Notes](paper_notes/disnet.md)] IROS 2018 [monocular distance]
- [BirdGAN: Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles](https://arxiv.org/abs/1904.08494) [[Notes](paper_notes/birdgan.md)] IROS 2019
- [Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints](https://arxiv.org/abs/1905.09970) [[Notes](paper_notes/shift_rcnn.md)] ICIP 2019
- [3D-RCNN: Instance-level 3D Object Reconstruction via Render-and-Compare](http://openaccess.thecvf.com/content_cvpr_2018/papers/Kundu_3D-RCNN_Instance-Level_3D_CVPR_2018_paper.pdf) [[Notes](paper_notes/3d_rcnn.md)] CVPR 2018
- [Deep Optics for Monocular Depth Estimation and 3D Object Detection](https://arxiv.org/abs/1904.08601) [[Notes](paper_notes/deep_optics.md)] ICCV 2019
- [MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation](https://arxiv.org/abs/1906.06059) [[Notes](paper_notes/monoloco.md)] ICCV 2019
- [Joint Monocular 3D Vehicle Detection and Tracking](https://arxiv.org/abs/1811.10742) [[Notes](paper_notes/mono_3d_tracking.md)] ICCV 2019 (Berkeley DeepDrive)
- [CasGeo: 3D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Results](https://arxiv.org/abs/1909.01867) [[Notes](paper_notes/casgeo.md)]
## 2019-09 (17)
- [Slimmable Neural Networks](https://arxiv.org/abs/1812.08928) [[Notes](paper_notes/slimmable_networks.md)] ICLR 2019
- [Universally Slimmable Networks and Improved Training Techniques](https://arxiv.org/abs/1903.05134) [[Notes](paper_notes/universal_slimmable.md)] ICCV 2019
- [AutoSlim: Towards One-Shot Architecture Search for Channel Numbers](https://arxiv.org/abs/1903.11728)
- [Once for All: Train One Network and Specialize it for Efficient Deployment](https://arxiv.org/pdf/1908.09791.pdf)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://vision.cornell.edu/se3/wp-content/uploads/2018/03/2666.pdf) [[Notes](paper_notes/dota.md)] CVPR 2018 (rotated bbox)
- [RoiTransformer: Learning RoI Transformer for Oriented Object Detection in Aerial Images](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_Learning_RoI_Transformer_for_Oriented_Object_Detection_in_Aerial_Images_CVPR_2019_paper.pdf) [[Notes](paper_notes/roi_transformer.md)] CVPR 2019 (rotated bbox)
- [RRPN: Arbitrary-Oriented Scene Text Detection via Rotation Proposals](https://arxiv.org/abs/1703.01086) TMM 2018
- [R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection](https://arxiv.org/abs/1706.09579) (rotated bbox)
- [TI white paper: Webinar: mmWave Radar for Automotive and Industrial applications
](https://training.ti.com/epd-pro-rap-mmwaveradar-adh-tr-webinar-eu) [[Notes](paper_notes/ti_mmwave_radar_webinar.md)] [TI, radar]
- [Federated Learning: Strategies for Improving Communication Efficiency](https://arxiv.org/abs/1610.05492) [[Notes](paper_notes/federated_learning_comm.md)] NIPS 2016
- [sort: Simple Online and Realtime Tracking](https://arxiv.org/abs/1602.00763) [[Notes](paper_notes/sort.md)] ICIP 2016
- [deep-sort: Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402) [[Notes](paper_notes/deep_sort.md)]
- [MT-CNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://kpzhang93.github.io/MTCNN_face_detection_alignment/) [[Notes](paper_notes/mtcnn.md)] SPL 2016 (real time, facial landmark)
- [RetinaFace: Single-stage Dense Face Localisation in the Wild](https://arxiv.org/abs/1905.00641) [[Notes](paper_notes/retina_face.md)] CVPR 2020 [joint object and landmark detection]
- [SC-SfM-Learner: Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://arxiv.org/abs/1908.10553) [[Notes](paper_notes/sc_sfm_learner.md)] NIPS 2019
- [SiamMask: Fast Online Object Tracking and Segmentation: A Unifying Approach](https://arxiv.org/abs/1812.05050) CVPR 2019 (tracking, segmentation, label propagation)
- [Review of Kálmán Filter](https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/) (from Tim Babb, Pixar Animation) [[Notes](paper_notes/kalman_filter.md)]
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://arxiv.org/abs/1605.06409) [[Notes](paper_notes/rfcn.md)] NIPS 2016
- [Guided backprop: Striving for Simplicity: The All Convolutional Net](https://arxiv.org/pdf/1412.6806.pdf) [[Notes](paper_notes/guided_backprop.md)] ICLR 2015
- [Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks](http://www.cs.cmu.edu/~mvo/index_files/Papers/ONet_19.pdf) [[Notes](paper_notes/occlusion_net.md)] CVPR 2019
- [Boxy Vehicle Detection in Large Images](https://boxy-dataset.com/boxy/index) [[Notes](paper_notes/boxy.md)] ICCV 2019
- [FQNet: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection](https://arxiv.org/abs/1904.12681) [[Notes](paper_notes/fqnet.md)] CVPR 2019 [Mono 3DOD, Jiwen Lu]
## 2019-08 (18)
- [Mono3D: Monocular 3D Object Detection for Autonomous Driving](https://www.cs.toronto.edu/~urtasun/publications/chen_etal_cvpr16.pdf) [[Notes](paper_notes/mono3d.md)] CVPR2016
- [MonoDIS: Disentangling Monocular 3D Object Detection](https://arxiv.org/abs/1905.12365) [[Notes](paper_notes/monodis.md)] ICCV 2019
- [Pseudo lidar-e2e: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud](https://arxiv.org/abs/1903.09847) [[Notes](paper_notes/pseudo_lidar_e2e.md)] ICCV 2019 (pseudo-lidar with 2d and 3d consistency loss, better than PL and worse than PL++, SOTA for pure mono3D)
- [MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization](https://arxiv.org/abs/1811.10247) [[Notes](paper_notes/monogrnet.md)] AAAI 2019 (SOTA of Mono3DOD, MLF < MonoGRNet < Pseudo-lidar)
- [MLF: Multi-Level Fusion based 3D Object Detection from Monocular Images](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Multi-Level_Fusion_Based_CVPR_2018_paper.pdf) [[Notes](paper_notes/mlf.md)] CVPR 2018 (precursor to pseudo-lidar)
- [ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape](https://arxiv.org/abs/1812.02781) [[Notes](paper_notes/roi10d.md)] CVPR 2019
- [AM3D: Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://arxiv.org/abs/1903.11444) [[Notes](paper_notes/am3d.md)] ICCV 2019 [similar to pseudo-lidar, color-enhanced]
- [Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors](https://arxiv.org/abs/1901.03446) [[Notes](paper_notes/mono3d++.md)] (from Stefano Soatto) AAAI 2019
- [Deep Metadata Fusion for Traffic Light to Lane Assignment](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8613841) [[Notes](paper_notes/deep_lane_association.md)] IEEE RA-L 2019 (traffic lights association)
- [Automatic Traffic Light to Ego Vehicle Lane Association at Complex Intersections](https://ieeexplore.ieee.org/document/8569421) ITSC 2019 (traffic lights association)
- [Distant Vehicle Detection Using Radar and Vision](https://arxiv.org/abs/1901.10951)[[Notes](paper_notes/distant_object_radar.md)] ICRA 2019 [radar, vision, radar tracklets fusion]
- [Distance Estimation of Monocular Based on Vehicle Pose Information](https://iopscience.iop.org/article/10.1088/1742-6596/1168/3/032040/pdf) [[Notes](paper_notes/distance_estimation_pose_radar.md)]
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://arxiv.org/abs/1705.07115) [[Notes](paper_notes/uncertainty_multitask.md)] CVPR 2018 (Alex Kendall)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://arxiv.org/abs/1711.02257) [[Notes](paper_notes/gradnorm.md)] ICML 2018 (multitask)
- [DTP: Dynamic Task Prioritization for Multitask Learning](http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Focus_on_the_ECCV_2018_paper.pdf) [[Notes](paper_notes/dtp.md)] ECCV 2018 [multitask, Stanford]
- [Will this car change the lane? - Turn signal recognition in the frequency domain](https://ieeexplore.ieee.org/document/6856477/) [[Notes](paper_notes/tsl_frequency.md)] IV 2014
- [Complex-YOLO: Real-time 3D Object Detection on Point Clouds](https://arxiv.org/abs/1803.06199) [[Notes](paper_notes/complex_yolo.md)] (BEV detection only)
- [Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds](https://arxiv.org/abs/1904.07537) CVPR 2019 (sensor fusion and tracking)
- [An intriguing failing of convolutional neural networks and the CoordConv solution](https://arxiv.org/abs/1807.03247) [[Notes](paper_notes/coord_conv.md)] NIPS 2018
## 2019-07 (19)
- [Deep Parametric Continuous Convolutional Neural Networks](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Deep_Parametric_Continuous_CVPR_2018_paper.pdf) [[Notes](paper_notes/parametric_cont_conv.md)] CVPR 2018 (@Uber, sensor fusion)
- [ContFuse: Deep Continuous Fusion for Multi-Sensor 3D Object Detection](http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Liang_Deep_Continuous_Fusion_ECCV_2018_paper.pdf) [[Notes](paper_notes/contfuse.md)] ECCV 2018 [Uber ATG, sensor fusion, BEV]
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf) [[Notes](paper_notes/faf.md)] CVPR 2018 oral [lidar only, perception and prediction]
- [LearnK: Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://arxiv.org/pdf/1904.04998.pdf) [[Notes](paper_notes/learnk.md)] ICCV 2019 [monocular depth estimation, intrinsic estimation, SOTA]
- [monodepth: Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://arxiv.org/abs/1609.03677) [[Notes](paper_notes/monodepth.md)] CVPR 2017 oral (monocular depth estimation, stereo for training)
- [Struct2depth: Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos](https://arxiv.org/abs/1811.06152) [[Notes](paper_notes/struct2depth.md)] AAAI 2019 [monocular depth estimation, estimating movement of dynamic object, infinite depth problem, online finetune]
- [Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency](https://arxiv.org/pdf/1711.03665.pdf) [[Notes](paper_notes/edge_aware_depth_normal.md)] AAAI 2018 (monocular depth estimation, static assumption, surface normal)
- [LEGO Learning Edge with Geometry all at Once by Watching Videos](https://arxiv.org/pdf/1803.05648.pdf) [[Notes](paper_notes/lego.md)] CVPR 2018 spotlight (monocular depth estimation, static assumption, surface normal)
- [Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network](https://arxiv.org/abs/1902.05394) [[Notes](paper_notes/radar_3d_od_fcn.md)] (radar, RD map, OD, Arxiv 201902)
- [A study on Radar Target Detection Based on Deep Neural Networks](https://www.researchgate.net/publication/330748053_A_Study_on_Radar_Target_Detection_Based_on_Deep_Neural_Networks) [[Notes](paper_notes/radar_target_detection_tsinghua.md)] (radar, RD map, OD)
- [2D Car Detection in Radar Data with PointNets](https://arxiv.org/abs/1904.08414) [[Notes](paper_notes/radar_detection_pointnet.md)] (from Ulm Univ, radar, point cloud, OD, Arxiv 201904)
- [Learning Confidence for Out-of-Distribution Detection in Neural Networks](https://arxiv.org/abs/1802.04865) [[Notes](paper_notes/learning_ood_conf.md)] (budget to cheat)
- [A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification](assets/papers/bosch_traffic_lights.pdf) [[Notes](paper_notes/bosch_traffic_lights.md)] ICRA 2017 (Bosch, traffic lights)
- [How hard can it be? Estimating the difficulty of visual search in an image](https://arxiv.org/abs/1705.08280) [[Notes](paper_notes/how_hard_can_it_be.md)] CVPR 2016
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://arxiv.org/pdf/1902.07830.pdf) [[Notes](paper_notes/deep_fusion_review.md)] (review from Bosch)
- [Review of monocular 3d object detection](https://zhuanlan.zhihu.com/p/57029694) (blog from 知乎)
- [Deep3dBox: 3D Bounding Box Estimation Using Deep Learning and Geometry](https://arxiv.org/abs/1612.00496) [[Notes](paper_notes/deep3dbox.md)] CVPR 2017 [Zoox]
- [MonoPSR: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction](https://arxiv.org/abs/1904.01690) [[Notes](paper_notes/monopsr.md)] CVPR 2019
- [OFT: Orthographic Feature Transform for Monocular 3D Object Detection](https://arxiv.org/abs/1811.08188) [[Notes](paper_notes/oft.md)] BMVC 2019 [Convert camera to BEV, Alex Kendall]
## 2019-06 (12)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://arxiv.org/abs/1905.02249) [[Notes](paper_notes/MixMatch.md)]
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/pdf/1905.11946.pdf) [[Notes](paper_notes/efficientnet.md)] ICML 2019
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://arxiv.org/abs/1703.04977) [[Notes](paper_notes/uncertainty_bdl.md)] NIPS 2017
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://arxiv.org/pdf/1511.02680.pdf) [[Notes](paper_notes/bayesian_segnet.md)]BMVC 2017
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://arxiv.org/pdf/1811.02146.pdf) [[Notes](paper_notes/trafficpredict.md)] AAAI 2019 oral
- [Deep Depth Completion of a Single RGB-D Image](https://arxiv.org/pdf/1803.09326.pdf) [[Notes](paper_notes/deep_depth_completion_rgbd.md)] CVPR 2018 (indoor)
- [DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image](https://arxiv.org/pdf/1812.00488v2.pdf) [[Notes](paper_notes/deeplidar.md)] CVPR 2019 (outdoor)
- [SfMLearner: Unsupervised Learning of Depth and Ego-Motion from Video](https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner/cvpr17_sfm_final.pdf) [[Notes](paper_notes/sfm_learner.md)] CVPR 2017
- [Monodepth2: Digging Into Self-Supervised Monocular Depth Estimation](https://arxiv.org/abs/1806.01260) [[Notes](paper_notes/monodepth2.md)] ICCV 2019 [Niantic]
- [DeepSignals: Predicting Intent of Drivers Through Visual Signals](https://arxiv.org/pdf/1905.01333.pdf) [[Notes](paper_notes/deep_signals.md)] ICRA 2019 (@Uber, turn signal detection)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://arxiv.org/abs/1904.01355) [[Notes](paper_notes/fcos.md)] ICCV 2019 [Chunhua Shen]
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://arxiv.org/abs/1906.06310) [[Notes](paper_notes/pseudo_lidar++.md)] ICLR 2020
- [MMF: Multi-Task Multi-Sensor Fusion for 3D Object Detection](http://www.cs.toronto.edu/~byang/papers/mmf.pdf) [[Notes](paper_notes/mmf.md)] CVPR 2019 (@Uber, sensor fusion)
## 2019-05 (18)
- [CenterNet: Objects as points](https://arxiv.org/abs/1904.07850) (from ExtremeNet authors) [[Notes](paper_notes/centernet.md)]
- [CenterNet: Object Detection with Keypoint Triplets](https://arxiv.org/abs/1904.08189) [[Notes](paper_notes/centernet_cas.md)]
- [Object Detection based on Region Decomposition and Assembly](https://arxiv.org/abs/1901.08225) [[Notes](paper_notes/object_detection_region_decomposition.md)] AAAI 2019
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://arxiv.org/abs/1803.03635) [[Notes](paper_notes/lottery_ticket_hypothesis.md)] ICLR 2019
- [M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network](https://arxiv.org/abs/1811.04533) [[Notes](paper_notes/m2det.md)] AAAI 2019
- [Deep Radar Detector](https://arxiv.org/abs/1906.12187) [[Notes](paper_notes/deep_radar_detector.md)] RadarCon 2019
- [Semantic Segmentation on Radar Point Clouds](https://ieeexplore.ieee.org/document/8455344) [[[Notes](paper_notes/radar_point_semantic_seg.md)]] (from Daimler AG) FUSION 2018
- [Pruning Filters for Efficient ConvNets](https://arxiv.org/pdf/1608.08710.pdf) [[Notes](paper_notes/pruning_filters.md)] ICLR 2017
- [Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks](https://arxiv.org/pdf/1810.00518.pdf) [[Notes](paper_notes/layer_compensated_pruning.md)] NIPS 2018 talk
- [LeGR: Filter Pruning via Learned Global Ranking](https://arxiv.org/pdf/1904.12368.pdf) [[Notes](paper_notes/legr.md)] CVPR 2020 oral
- [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://arxiv.org/pdf/1904.07392.pdf) [[Notes](paper_notes/nas_fpn.md)] CVPR 2019
- [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501) [[Notes](paper_notes/autoaugment.md)] CVPR 2019
- [Path Aggregation Network for Instance Segmentation](https://arxiv.org/pdf/1803.01534.pdf) [[Notes](paper_notes/panet.md)] CVPR 2018
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://arxiv.org/pdf/1707.06168.pdf) ICCV 2017 (Face++, Yihui He) [[Notes](paper_notes/channel_pruning_megvii.md)]
- [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://arxiv.org/pdf/1802.03494.pdf) ECCV 2018 (Song Han, Yihui He)
- [MobileNetV3: Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) [[Notes](paper_notes/mobilenets_v3.md)] ICCV 2019
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/pdf/1807.11626.pdf) [[Notes](paper_notes/mnasnet.md)] CVPR 2019
- [Rethinking the Value of Network Pruning](https://arxiv.org/pdf/1810.05270.pdf) ICLR 2019
## 2019-04 (12)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) (MobileNets v2) [[Notes](paper_notes/mobilenets_v2.md)] CVPR 2018
- [A New Performance Measure and Evaluation Benchmark
for Road Detection Algorithms](http://www.cvlibs.net/publications/Fritsch2013ITSC.pdf) [[Notes](paper_notes/kitti_lane.md)] ITSC 2013
- [MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving](https://arxiv.org/pdf/1612.07695.pdf) [[Notes](paper_notes/multinet_raquel.md)]
- [Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction](https://arxiv.org/pdf/1803.08707.pdf) (Very nice illustration of 1 and 2 stage object detection)
- [Light-Head R-CNN: In Defense of Two-Stage Object Detector](https://arxiv.org/pdf/1711.07264.pdf) [[Notes](paper_notes/lighthead_rcnn.md)] (from Megvii)
- [CSP: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection](https://arxiv.org/abs/1904.02948) [[Notes](paper_notes/csp_pedestrian.md)] CVPR 2019 [center and scale prediction, anchor-free, near SOTA pedestrian]
- [Review of Anchor-free methods (知乎Blog) 目标检测：Anchor-Free时代](https://zhuanlan.zhihu.com/p/62103812) [Anchor free深度学习的目标检测方法](https://zhuanlan.zhihu.com/p/64563186) [My Slides on CSP](https://docs.google.com/presentation/d/1_dUfxv63108bZXUnVYPIOAdEIkRZw5BR9-rOp-Ni0X0/)
- [DenseBox: Unifying Landmark Localization with End to End Object Detection](https://arxiv.org/pdf/1509.04874.pdf)
- [CornerNet: Detecting Objects as Paired Keypoints](https://arxiv.org/pdf/1808.01244.pdf) [[Notes](paper_notes/cornernet.md)] ECCV 2018
- [ExtremeNet: Bottom-up Object Detection by Grouping Extreme and Center Points](https://arxiv.org/pdf/1901.08043.pdf) [[Notes](paper_notes/extremenet.md)] CVPR 2019
- [FSAF: Feature Selective Anchor-Free Module for Single-Shot Object Detection](https://arxiv.org/pdf/1903.00621.pdf) [[Notes](paper_notes/fsaf_detection.md)] CVPR 2019
- [FoveaBox: Beyond Anchor-based Object Detector](https://arxiv.org/pdf/1904.03797v1.pdf) (anchor-free) [[Notes](paper_notes/foveabox.md)]
## 2019-03 (19)
- [Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/pdf/1902.04103.pdf) [[Notes](paper_notes/bag_of_freebies_object_detection.md)]
- [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/pdf/1710.09412.pdf) [[Notes](paper_notes/mixup.md)] ICLR 2018
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Su_Multi-View_Convolutional_Neural_ICCV_2015_paper.pdf) (MVCNN) [[Notes](paper_notes/mvcnn.md)] ICCV 2015
- [3D ShapeNets: A Deep Representation for Volumetric Shapes](http://3dshapenets.cs.princeton.edu/paper.pdf) [[Notes](paper_notes/3d_shapenets.md)] CVPR 2015
- [Volumetric and Multi-View CNNs for Object Classification on 3D Data](https://arxiv.org/pdf/1604.03265.pdf) [[Notes](paper_notes/vol_vs_mvcnn.md)] CVPR 2016
- [Group Normalization](https://arxiv.org/pdf/1803.08494.pdf) [[Notes](paper_notes/groupnorm.md)] ECCV 2018
- [Spatial Transformer Networks](https://arxiv.org/pdf/1506.02025.pdf) [[Notes](paper_notes/stn.md)] NIPS 2015
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://arxiv.org/pdf/1711.08488.pdf) (F-PointNet) [[Notes](paper_notes/frustum_pointnet.md)] CVPR 2018
- [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/pdf/1801.07829.pdf) [[Notes](paper_notes/edgeconv.md)]
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://arxiv.org/abs/1812.04244) (SOTA for 3D object detection) [[Notes](paper_notes/point_rcnn.md)] CVPR 2019
- [MV3D: Multi-View 3D Object Detection Network for Autonomous Driving](https://arxiv.org/abs/1611.07759) [[Notes](paper_notes/mv3d.md)] CVPR 2017 (Baidu, sensor fusion, BV proposal)
- [AVOD: Joint 3D Proposal Generation and Object Detection from View Aggregation](https://arxiv.org/abs/1712.02294) [[Notes](paper_notes/avod.md)] IROS 2018 (sensor fusion, multiview proposal)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) [[Notes](paper_notes/mobilenets.md)]
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gafp in 3D Object Detection for Autonomous Driving](https://arxiv.org/abs/1812.07179) [[Notes](paper_notes/pseudo_lidar.md)] CVPR 2019
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://arxiv.org/pdf/1711.06396.pdf) CVPR 2018 (Apple, first end-to-end point cloud encoding to grid)
- [SECOND: Sparsely Embedded Convolutional Detection](https://www.mdpi.com/1424-8220/18/10/3337/pdf) Sensors 2018 (builds on VoxelNet)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://arxiv.org/abs/1812.05784) [[Notes](paper_notes/point_pillars.md)] CVPR 2019 (builds on SECOND)
- [Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite](http://www.cvlibs.net/publications/Geiger2012CVPR.pdf) [[Notes](paper_notes/kitti.md)] CVPR 2012
- [Vision meets Robotics: The KITTI Dataset](http://ww.cvlibs.net/publications/Geiger2013IJRR.pdf) [[Notes](paper_notes/kitti.md)] IJRR 2013
## 2019-02 (9)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://arxiv.org/pdf/1705.07750.pdf) (I3D) [[Notes](paper_notes/quo_vadis_i3d.md)]Video CVPR 2017
- [Initialization Strategies of Spatio-Temporal Convolutional Neural Networks](https://arxiv.org/pdf/1503.07274.pdf) [[Notes](paper_notes/quo_vadis_i3d.md)] Video
- [Detect-and-Track: Efficient Pose Estimation in Videos](https://arxiv.org/pdf/1712.09184.pdf) [[Notes](paper_notes/quo_vadis_i3d.md)] ICCV 2017 Video
- [Deep Learning Based Rib Centerline Extraction and Labeling](https://arxiv.org/pdf/1809.07082) [[Notes](paper_notes/rib_centerline_philips.md)] MI MICCAI 2018
- [SlowFast Networks for Video Recognition](https://arxiv.org/pdf/1812.03982.pdf) [[Notes](paper_notes/slowfast.md)] ICCV 2019 Oral
- [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf) (ResNeXt) [[Notes](paper_notes/resnext.md)] CVPR 2017
- [Beyond the pixel plane: sensing and learning in 3D](https://thegradient.pub/beyond-the-pixel-plane-sensing-and-learning-in-3d/) (blog, [中文版本](https://zhuanlan.zhihu.com/p/44386618))
- [VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition](https://www.ri.cmu.edu/pub_files/2015/9/voxnet_maturana_scherer_iros15.pdf) (VoxNet) [[Notes](paper_notes/voxnet.md)]
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://arxiv.org/pdf/1612.00593.pdf) CVPR 2017 [[Notes](paper_notes/pointnet.md)]
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://arxiv.org/pdf/1706.02413.pdf) NIPS 2017 [[Notes](paper_notes/pointnet++.md)]
- [Review of Geometric deep learning 几何深度学习前沿 (from 知乎)](https://zhuanlan.zhihu.com/p/36888114) (Up to CVPR 2018)
## 2019-01 (10)
- [DQN: Human-level control through deep reinforcement learning (Nature DQN paper)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) [[Notes](paper_notes/nature_dqn_paper.md)] DRL
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://arxiv.org/pdf/1811.08661.pdf) [[Notes](paper_notes/retina_unet.md)] MI
- [Panoptic Segmentation](https://arxiv.org/pdf/1801.00868.pdf) [[Notes](paper_notes/panoptic_segmentation.md)] PanSeg
- [Panoptic Feature Pyramid Networks](https://arxiv.org/pdf/1901.02446.pdf) [[Notes](paper_notes/panoptic_fpn.md)] PanSeg
- [Attention-guided Unified Network for Panoptic Segmentation](https://arxiv.org/pdf/1812.03904.pdf) [[Notes](paper_notes/AUNet_panoptic.md)] PanSeg
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://arxiv.org/pdf/1812.01187.pdf) [[Notes](paper_notes/bag_of_tricks_cnn.md)] CLS
- [Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes](https://link.springer.com/chapter/10.1007/978-3-030-00937-3_86) [[Notes](paper_notes/drl_vessel_centerline.md)] DRL MI
- [Deep Reinforcement Learning for Flappy Bird](http://cs229.stanford.edu/proj2015/362_report.pdf) [[Notes](paper_notes/drl_flappy.md)] DRL
- [Long-Term Feature Banks for Detailed Video Understanding](https://arxiv.org/pdf/1812.05038.pdf) [[Notes](paper_notes/long_term_feat_bank.md)] Video
- [Non-local Neural Networks](https://arxiv.org/pdf/1711.07971.pdf) [[Notes](paper_notes/non_local_net.md)] Video CVPR 2018
## 2018
- [Mask R-CNN](https://arxiv.org/pdf/1703.06870.pdf)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://arxiv.org/pdf/1712.00726.pdf)
- [Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf) (RetinaNet) [[Notes](paper_notes/focal_loss.md)]
- [Squeeze-and-Excitation Networks](https://arxiv.org/pdf/1709.01507) (SENet)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/pdf/1710.10196.pdf)
- [Deformable Convolutional Networks](https://arxiv.org/abs/1703.06211) ICCV 2017 [build on R-FCN]
- [Learning Region Features for Object Detection](https://arxiv.org/pdf/1803.07066.pdf)
## 2017 and before
- [Learning notes on Deep Learning](Learning_notes.md)
- [List of Papers on Machine Learning](List_of_Machine_Learning_Papers.md)
- [Notes of Literature Review on CNN in CV](paper_notes/cnn_papers.md) This is the notes for all the papers in the recommended list [here](papers_and_books_to_start.md)
- [Notes of Literature Review (Others)](misc.md)
- [Notes on how to set up DL/ML environment](ML_DL_environment_Setup.md)
- [Useful setup notes](installation_log.md)
## Papers to Read
Here is the list of papers waiting to be read.
### Deep Learning in general
- [SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving](https://arxiv.org/pdf/1612.01051.pdf)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/pdf/1706.02677.pdf)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://openreview.net/forum?id=Bygh9j09KX) ICML 2019
- [Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet](https://openreview.net/forum?id=SkfMWhAqYQ) (BagNet) [blog](https://blog.evjang.com/2019/02/bagnet.html) ICML 2019
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://arxiv.org/pdf/1803.09820v2.pdf)
- [Understanding deep learning requires rethinking generalization](https://arxiv.org/pdf/1611.03530.pdf)
- [Gradient Reversal: Unsupervised Domain Adaptation by Backpropagation](https://arxiv.org/abs/1409.7495) ICML 2015
### Self-training
- [Rethinking Pre-training and Self-training](https://arxiv.org/abs/2006.06882) NeurIPS 2020 [Quoc Le]
### 2D Object Detection and Segmentation
- [Mask Scoring R-CNN](https://arxiv.org/pdf/1903.00241.pdf) CVPR 2019
- [Training Region-based Object Detectors with Online Hard Example Mining](https://arxiv.org/pdf/1604.03540.pdf)
- [Gliding vertex on the horizontal bounding box for multi-oriented object detection](https://arxiv.org/abs/1911.09358)
- [ONCE: Incremental Few-Shot Object Detection](https://arxiv.org/abs/2003.04668) CVPR 2020
- [Domain Adaptive Faster R-CNN for Object Detection in the Wild](https://arxiv.org/abs/1803.03243) CVPR 2018
- [Foggy Cityscapes: Semantic Foggy Scene Understanding with Synthetic Data](https://arxiv.org/abs/1708.07819) IJCV 2018
- [Foggy Cityscapes ECCV: Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding](https://arxiv.org/abs/1808.01265) ECCV 2018
- [Dropout Sampling for Robust Object Detection in Open-Set Conditions](https://arxiv.org/abs/1710.06677) ICRA 2018 (Niko Sünderhauf)
- [Hybrid Task Cascade for Instance Segmentation](https://arxiv.org/abs/1901.07518) CVPR 2019 (cascaded mask RCNN)
- [Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection](https://arxiv.org/abs/1809.06006) ICRA 2019 (Niko Sünderhauf)
- [A Unified Panoptic Segmentation Network](https://arxiv.org/pdf/1901.03784.pdf) CVPR 2019 PanSeg
- [Model Vulnerability to Distributional Shifts over Image Transformation Sets](https://arxiv.org/pdf/1903.11900.pdf) (CVPR workshop) [tl:dr](https://www.reddit.com/r/MachineLearning/comments/b81uwq/r_model_vulnerability_to_distributional_shifts/)
- [Automatic adaptation of object detectors to new domains using self-training](https://arxiv.org/pdf/1904.07305.pdf) CVPR 2019 (find corner case and boost)
- [Missing Labels in Object Detection](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Weakly%20Supervised%20Learning%20for%20Real-World%20Computer%20Vision%20Applications/Xu_Missing_Labels_in_Object_Detection_CVPRW_2019_paper.pdf) CVPR 2019
- [DenseBox: Unifying Landmark Localization with End to End Object Detection](https://arxiv.org/pdf/1509.04874.pdf)
- [Circular Object Detection in Polar Coordinates for 2D LIDAR Data](https://www.researchgate.net/publication/309365539_Circular_Object_Detection_in_Polar_Coordinates_for_2D_LIDAR_DataCCPR2016) CCPR 2016
- [LFFD: A Light and Fast Face Detector for Edge Devices](https://arxiv.org/abs/1904.10633) [Lightweight, face detection, car detection]
- [UnitBox: An Advanced Object Detection Network](https://arxiv.org/abs/1608.01471) ACM MM 2016 [Ln IoU loss, Thomas Huang]
### Fisheye
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://arxiv.org/abs/2012.02124) WACV 2021
### Video Understanding
- [Learning Spatiotemporal Features with 3D Convolutional Networks](https://arxiv.org/pdf/1412.0767.pdf) (C3D) Video ICCV 2015
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://arxiv.org/pdf/1705.08421.pdf)
- [Spatiotemporal Residual Networks for Video Action Recognition](https://arxiv.org/pdf/1611.02155.pdf) (decouple spatiotemporal) NIPS 2016
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://arxiv.org/pdf/1711.10305.pdf) (P3D, decouple spatiotemporal) ICCV 2017
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/pdf/1711.11248.pdf) (decouple spatiotemporal) CVPR 2018
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://arxiv.org/pdf/1712.04851.pdf) (decouple spatiotemporal) ECCV 2018
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://arxiv.org/pdf/1711.09577.pdf) CVPR 2018
- [AGSS-VOS: Attention Guided Single-Shot Video Object Segmentation](http://openaccess.thecvf.com/content_ICCV_2019/papers/Lin_AGSS-VOS_Attention_Guided_Single-Shot_Video_Object_Segmentation_ICCV_2019_paper.pdf) ICCV 2019
- [One-Shot Video Object Segmentation](https://arxiv.org/abs/1611.05198) CVPR 2017
- [Looking Fast and Slow: Memory-Guided Mobile Video Object Detection](https://arxiv.org/abs/1903.10172) CVPR 2018
- [Towards High Performance Video Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhu_Towards_High_Performance_CVPR_2018_paper.pdf) [[Notes](paper_notes/high_performance_video_od.md)] CVPR 2018
- [Towards High Performance Video Object Detection for Mobiles](https://arxiv.org/abs/1804.05830) [[Notes](paper_notes/high_performance_video_od_mobile.md)]
- [Temporally Distributed Networks for Fast Video Semantic Segmentation](https://arxiv.org/abs/2004.01800) CVPR 2020 [efficient video segmentation]
- [Memory Enhanced Global-Local Aggregation for Video Object Detection](https://arxiv.org/abs/2003.12063) CVPR 2020 [efficient video object detection]
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and
Detection with Hierarchical Aggregation](https://arxiv.org/abs/1804.06055) IJCAI 2018 oral [video skeleton]
- [RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving](https://arxiv.org/abs/1912.00438) NeurIPS 2019 workshop
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://arxiv.org/abs/1411.4389) CVPR 2015 oral
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://arxiv.org/abs/1608.00859) ECCV 2016
- [TRN: Temporal Relational Reasoning in Videos](https://arxiv.org/abs/1711.08496) ECCV 2018
- [X3D: Expanding Architectures for Efficient Video Recognition](https://arxiv.org/abs/2004.04730) CVPR 2020 oral [FAIR]
- [Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians](https://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Temporal-Context_Enhanced_Detection_of_Heavily_Occluded_Pedestrians_CVPR_2020_paper.html) CVPR 2020 oral [pedestrian, video]
- [Flow-guided feature aggregation for video object detection](https://arxiv.org/abs/1703.10025) ICCV 2017 [video, object detection]
- [3D human pose estimation in video with temporal convolutions and
semi-supervised training](http://openaccess.thecvf.com/content_CVPR_2019/papers/Pavllo_3D_Human_Pose_Estimation_in_Video_With_Temporal_Convolutions_and_CVPR_2019_paper.pdf) CVPR 2019 [mono3D pose estimation from video]
- [OmegaNet: Distilled Semantics for Comprehensive Scene Understanding from Videos](https://arxiv.org/abs/2003.14030) CVPR 2020
- [Object Detection in Videos with Tubelet Proposal Networks](https://arxiv.org/abs/1702.06355) CVPR 2017 [video object detection]
- [T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos](https://arxiv.org/abs/1604.02532) [video object detection]
- [Flow-Guided Feature Aggregation for Video Object Detection](https://arxiv.org/abs/1703.10025) ICCV 2017 [Jifeng Dai]
### Pruning and Compression
- [Efficient Deep Learning Inference based on Model Compression](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w33/Zhang_Efficient_Deep_Learning_CVPR_2018_paper.pdf) (Model Compression)
- [Neural Network Distiller](https://intellabs.github.io/distiller/algo_pruning.html) [Intel]
### Architecture Improvements
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/pdf/1803.02579.pdf)
- [CBAM: Convolutional Block Attention Module](https://arxiv.org/pdf/1807.06521.pdf)
### Reinforcement Learning
- [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) NIPS 2013
- [Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scan](http://comaniciu.net/Papers/MultiscaleDeepReinforcementLearning_PAMI18.pdf)
- [An Artificial Agent for Robust Image Registration](https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/download/14751/14296)
### 3D Perception
- [3D-CNN：3D Convolutional Neural Networks for Landing Zone Detection from LiDAR](https://www.ri.cmu.edu/pub_files/2015/3/maturana-root.pdf)
- [Generative and Discriminative Voxel Modeling with Convolutional Neural Networks](https://arxiv.org/pdf/1608.04236.pdf)
- [Orientation-boosted Voxel Nets for 3D Object Recognition](https://arxiv.org/pdf/1604.03351.pdf) (ORION)
- [GIFT: A Real-time and Scalable 3D Shape Search Engine](https://arxiv.org/pdf/1604.01879.pdf) CVPR 2016
- [3D Shape Segmentation with Projective Convolutional Networks](https://people.cs.umass.edu/~kalo/papers/shapepfcn/) (ShapePFCN)CVPR 2017
- [Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks](https://arxiv.org/pdf/1706.04496.pdf)
- [Open3D: A Modern Library for 3D Data Processing](http://www.open3d.org/wordpress/wp-content/paper.pdf)
- [Multimodal Deep Learning for Robust RGB-D Object Recognition](https://arxiv.org/pdf/1507.06821.pdf) IROS 2015
- [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://arxiv.org/pdf/1806.01411.pdf) CVPR 2019
- [Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling](https://arxiv.org/pdf/1712.06760.pdf) CVPR 2018 (Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds)
- [PU-Net: Point Cloud Upsampling Network](https://arxiv.org/pdf/1801.06761.pdf) CVPR 2018
- [Recurrent Slice Networks for 3D Segmentation of Point Clouds](https://arxiv.org/pdf/1802.04402.pdf) CVPR 2018
- [SPLATNet: Sparse Lattice Networks for Point Cloud Processing](https://arxiv.org/pdf/1802.08275.pdf) CVPR 2018
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/pdf/1606.09375.pdf) NIPS 2016
- [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/pdf/1609.02907.pdf) ICLR 2017
- [Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks](https://arxiv.org/pdf/1704.06803.pdf) NIPS 2017
- [Graph Attention Networks](https://arxiv.org/pdf/1710.10903.pdf) ICLR 2018
- [3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection](https://arxiv.org/pdf/1711.00238.pdf) (3D SSD)
- [Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models](https://arxiv.org/pdf/1704.01222.pdf) ICCV 2017
- [Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis](https://arxiv.org/pdf/1612.00101.pdf) CVPR 2017
- [IPOD: Intensive Point-based Object Detector for Point Cloud](https://arxiv.org/pdf/1812.05276.pdf)
- [Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images](https://cis.temple.edu/~latecki/Papers/DengCVPR2017.pdf) CVPR 2017
- [2D-Driven 3D Object Detection in RGB-D Images](http://openaccess.thecvf.com/content_ICCV_2017/papers/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.pdf)
- [3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection](https://arxiv.org/pdf/1711.00238.pdf)
- [Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection](https://arxiv.org/abs/2006.04356) [classify occluded object]
### Stereo and Flow
- [PSMNet: Pyramid Stereo Matching Network](https://arxiv.org/pdf/1803.08669.pdf) CVPR 2018
- [Stereo R-CNN based 3D Object Detection for Autonomous Driving](https://arxiv.org/pdf/1902.09738.pdf) CVPR 2019
- [Deep Rigid Instance Scene Flow](https://people.csail.mit.edu/weichium/papers/cvpr19-dsisf/paper.pdf) CVPR 2019
- [Upgrading Optical Flow to 3D Scene Flow through Optical Expansion](http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Upgrading_Optical_Flow_to_3D_Scene_Flow_Through_Optical_Expansion_CVPR_2020_paper.pdf) CVPR 2020
- [Learning Multi-Object Tracking and Segmentation from Automatic Annotations](https://arxiv.org/abs/1912.02096) CVPR 2020 [automatic MOTS annotation]
### Traffic light and traffic sign
- [Traffic-Sign Detection and Classification in the Wild](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf) CVPR 2016 [Tsinghua, Tencent, traffic signs]
- [A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint
Traffic Sign and Light Detection](https://arxiv.org/abs/1806.07987) IEEE CRV 2018 [U torronto]
- [Detecting Traffic Lights by Single Shot Detection](https://arxiv.org/abs/1805.02523) ITSC 2018
- [DeepTLR: A single Deep Convolutional Network for Detection and Classification of Traffic Lights](https://sci-hub.st/10.1109/IVS.2016.7535408) IV 2016
- [Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data](https://openaccess.thecvf.com/content_cvpr_2017_workshops/w9/papers/Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paper.pdf) CVPR 2017 workshop
- [Traffic light recognition in varying illumination using deep learning and saliency map](https://www.researchgate.net/profile/Vijay_John3/publication/265014373_Traffic_Light_Recognition_in_Varying_Illumination_using_Deep_Learning_and_Saliency_Map/links/56aac00408ae8f3865666102.pdf) ITSC 2014 [traffic light]
- [Traffic light recognition using high-definition map features](https://sci-hub.st/https://www.sciencedirect.com/science/article/abs/pii/S0921889018301234) RAS 2019
- [Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives](http://cvrr.ucsd.edu/publications/2016/trafficSignalsITSTrans2016.pdf) TITS 2015
### Datasets and Surveys
- [The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets](https://ieeexplore.ieee.org/document/8460737) ICRA 2018
- [The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset](https://arxiv.org/abs/1909.01300)
- [Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives](http://cvrr.ucsd.edu/publications/2016/trafficSignalsITSTrans2016.pdf) (traffic light survey, UCSD LISA)
- [Review of Graph Spectrum Theory](paper_notes/graph_spectrum.md) (WIP)
- [3D Deep Learning Tutorial at CVPR 2017](https://www.youtube.com/watch?v=8CenT_4HWyY) [[Notes](paper_notes/3ddl_cvpr2017.md)] - (WIP)
- [A Survey on Neural Architecture Search](https://arxiv.org/pdf/1905.01392.pdf)
- [Network pruning tutorial](https://jacobgil.github.io/deeplearning/pruning-deep-learning) (blog)
- [GNN tutorial at CVPR 2019](https://xiaolonw.github.io/graphnn/)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://arxiv.org/abs/2104.10133) [Waymo, prediction dataset]
- [PANDA: A Gigapixel-level Human-centric Video Dataset](https://arxiv.org/abs/2003.04852) CVPR 2020
- [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://openaccess.thecvf.com/content_ICCV_2019/papers/Yogamani_WoodScape_A_Multi-Task_Multi-Camera_Fisheye_Dataset_for_Autonomous_Driving_ICCV_2019_paper.pdf) ICCV 2019 [Valeo]
### Unsupervised depth estimation
- [Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation](https://arxiv.org/pdf/1808.00769.pdf) 3DV 2018
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://arxiv.org/pdf/1406.2283.pdf) NIPS 2014 (Eigen et al)
- [Learning Depth from Monocular Videos using Direct Methods](https://arxiv.org/abs/1712.00175) CVPR 2018 (monocular depth estimation)
- [Virtual-Normal: Enforcing geometric constraints of virtual normal for depth prediction](https://arxiv.org/abs/1907.12209) [[Notes](paper_notes/virtual_normal.md)] ICCV 2019 (better generation of PL)
- [Spatial Correspondence with Generative Adversarial Network: Learning Depth from Monocular Videos](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wu_Spatial_Correspondence_With_Generative_Adversarial_Network_Learning_Depth_From_Monocular_ICCV_2019_paper.pdf) ICCV 2019
- [Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM](http://openaccess.thecvf.com/content_ICCV_2019/papers/Sheng_Unsupervised_Collaborative_Learning_of_Keyframe_Detection_and_Visual_Odometry_Towards_ICCV_2019_paper.pdf) ICCV 2019
- [Visualization of Convolutional Neural Networks for Monocular Depth Estimation](https://arxiv.org/abs/1904.03380) ICCV 2019
### Indoor Depth
- [Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation](https://arxiv.org/abs/1905.08598) ICCV 2019 workshop [indoor]
- [Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2890_ECCV_2020_paper.php) ECCV 2020 [indoor depth]
- [Disambiguating Monocular Depth Estimation with a Single Transient](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3668_ECCV_2020_paper.php) ECCV 2020 [additional laser sensor, indoor depth]
- [Guiding Monocular Depth Estimation Using Depth-Attention Volume](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5491_ECCV_2020_paper.php) ECCV 2020 [indoor depth]
- [Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets](https://arxiv.org/abs/2007.11256) ECCV 2020 [indoor depth]
- [CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss](https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3365_ECCV_2020_paper.php) ECCV 2020 [indoor depth]
### lidar
- [PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/1807.00652.pdf) (pointnet alternative, backbone)
- [Vehicle Detection from 3D Lidar Using Fully Convolutional Network](https://arxiv.org/pdf/1608.07916.pdf) (VeloFCN) RSS 2016
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://arxiv.org/abs/1904.08889) (from the authors of PointNet)
- [PointCNN: Convolution On X-Transformed Points](https://arxiv.org/pdf/1801.07791.pdf) NIPS 2018
- [L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving](https://songshiyu01.github.io/pdf/L3Net_W.Lu_Y.Zhou_S.Song_CVPR2019.pdf) CVPR 2019
- [RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement](https://arxiv.org/abs/1811.03818) (sensor fusion, 3D mono proposal, refined in point cloud)
- [DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map](https://arxiv.org/pdf/1805.04949.pdf) CVPR 2018
- [Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection](https://arxiv.org/abs/1903.01864) IROS 2019
- [PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing](https://arxiv.org/abs/1910.08287)
- [Gated2Depth: Real-time Dense Lidar from Gated Images](https://arxiv.org/abs/1902.04997) ICCV 2019 oral
- [A Multi-Sensor Fusion System for Moving Object Detection and Tracking in Urban Driving Environments](http://www.cs.cmu.edu/~youngwoo/doc/icra-14-sensor-fusion.pdf) ICRA 2014
- [PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation](https://arxiv.org/abs/1711.10871) CVPR 2018 [sensor fusion, Zoox]
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://arxiv.org/abs/1904.09664) ICCV 2019 [Charles Qi]
- [StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation](http://www.bmva.org/bmvc/2015/papers/paper109/paper109.pdf)
- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation](https://arxiv.org/abs/2003.14032) CVPR 2020
- [Depth Sensing Beyond LiDAR Range](https://arxiv.org/abs/2004.03048) CVPR 2020 [wide baseline stereo with trifocal]
- [Probabilistic Semantic Mapping for Urban Autonomous Driving Applications](https://arxiv.org/abs/2006.04894) IROS 2020 [lidar mapping]
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://arxiv.org/abs/1911.11236) CVPR 2020 oral [lidar segmentation]
- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation](https://arxiv.org/abs/2003.14032) CVPR 2020 [lidar segmentation]
- [OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression](https://arxiv.org/abs/2005.07178) CVPR 2020 oral [lidar compression]
- [MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models](https://arxiv.org/abs/2011.07590) NeurIPS 2020 oral [lidar compression]
### Egocentric bbox prediction
- [Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty](https://arxiv.org/abs/1711.09026) CVPR 2018 [on-board bbox prediction]
- [Unsupervised Traffic Accident Detection in First-Person Videos](https://arxiv.org/abs/1903.00618) IROS 2019 (Honda)
- [NEMO: Future Object Localization Using Noisy Ego Priors](https://arxiv.org/abs/1909.08150) (Honda)
- [Robust Aleatoric Modeling for Future Vehicle Localization](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Precognition/Hudnell_Robust_Aleatoric_Modeling_for_Future_Vehicle_Localization_CVPRW_2019_paper.pdf) (perspective)
- [Multiple Object Forecasting: Predicting Future Object Locations in Diverse
Environments](https://arxiv.org/abs/1909.11944) WACV 2020 (perspective bbox, pedestrian)
- [Using panoramic videos for multi-person localization and tracking in a 3D panoramic coordinate](https://arxiv.org/abs/1911.10535)
### Lane Detection
- [End-to-end Lane Detection through Differentiable Least-Squares Fitting](https://arxiv.org/abs/1902.00293) ICCV 2019
- [Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit](https://doi.org/10.1109/TITS.2019.2890870) TITS 2019 [object-like proposals]
- [Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers](https://arxiv.org/abs/2003.08550) [3D LLD]
- [Ultra Fast Structure-aware Deep Lane Detection](https://arxiv.org/abs/2004.11757) ECCV 2020 [lane detection]
- [A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network](https://ieeexplore.ieee.org/document/8500551/) (convert camera to BEV)
- [FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network](https://arxiv.org/abs/1905.04354)
### Tracking
- [RetinaTrack: Online Single Stage Joint Detection and Tracking](https://arxiv.org/abs/2003.13870) CVPR 2020
- [Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art](https://arxiv.org/abs/1704.05519v2) (latest update in Dec 2019)
- [Simultaneous Identification and Tracking of Multiple People Using Video and IMUs](http://openaccess.thecvf.com/content_CVPRW_2019/papers/BMTT/Henschel_Simultaneous_Identification_and_Tracking_of_Multiple_People_Using_Video_and_CVPRW_2019_paper.pdf) CVPR 2019
- [Detect-and-Track: Efficient Pose Estimation in Videos](http://openaccess.thecvf.com/content_cvpr_2018/papers/Girdhar_Detect-and-Track_Efficient_Pose_CVPR_2018_paper.pdf)
- [TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis](https://arxiv.org/abs/1902.01466)
- [Video Action Transformer Network](https://arxiv.org/abs/1812.02707) CVPR 2019 oral
- [Online Real-time Multiple Spatiotemporal Action Localisation and Prediction](https://arxiv.org/abs/1611.08563) ICCV 2017
- [多目标跟踪 近年论文及开源代码汇总](https://zhuanlan.zhihu.com/p/65177442)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://arxiv.org/abs/2006.07327) CVPR 2020 oral [3DMOT, CMU, Kris Kitani]
- [Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking](https://arxiv.org/abs/2007.14557) ECCV 2020 spotlight [MOT, Tencent]
- [Towards Real-Time Multi-Object Tracking](https://arxiv.org/abs/1909.12605) ECCV 2020 [MOT]
- [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://arxiv.org/abs/2001.05673) [TRI]
### keypoints: pose and face
- [Probabilistic Face Embeddings](https://arxiv.org/abs/1904.09658) ICCV 2019
- [Data Uncertainty Learning in Face Recognition](https://arxiv.org/abs/2003.11339) CVPR 2020
- [Self-Supervised Learning of Interpretable Keypoints From Unlabelled Videos](http://openaccess.thecvf.com/content_CVPR_2020/html/Jakab_Self-Supervised_Learning_of_Interpretable_Keypoints_From_Unlabelled_Videos_CVPR_2020_paper.html) CVPR 2020 oral [VGG, self-supervised, interpretable, discriminator]
### General DL
- [Revisiting Small Batch Training for Deep Neural Networks](https://arxiv.org/abs/1804.07612)
- [ICML2019 workshop: Adaptive and Multitask Learning: Algorithms & Systems](https://icml.cc/Conferences/2019/ScheduleMultitrack?event=3504) ICML 2019
- [Adaptive Scheduling for Multi-Task Learning](https://marcpickett.com/cl2018/CL-2018_paper_82.pdf) NIPS 2018 (NMT)
- [Polar Transformer Networks](https://arxiv.org/abs/1709.01889) ICLR 2018
- [Measuring Calibration in Deep Learning](https://arxiv.org/abs/1904.01685) CVPR 2019
- [Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation](https://arxiv.org/abs/1908.00598) ICCV 2019 (epistemic uncertainty)
- [Making Convolutional Networks Shift-Invariant Again](https://arxiv.org/abs/1904.11486) ICML
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://arxiv.org/abs/1906.12340) NeurIPS 2019
- [Understanding deep learning requires rethinking generalization](https://arxiv.org/abs/1611.03530) ICLR 2017 [ICLR best paper]
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://arxiv.org/abs/1610.02136) ICLR 2017 (NLL score as anomaly score)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0801.pdf) CVPR 2018 spotlight (Stella Yu)
- [Theoretical insights into the optimization landscape of over-parameterized shallow neural networks](https://arxiv.org/abs/1707.04926) TIP 2018
- [The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning](https://arxiv.org/abs/1712.06559) ICML 2018
- [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) CVPR 2020
- [Moco2: Improved Baselines with Momentum Contrastive Learning](https://arxiv.org/abs/2003.04297)
- [SGD on Neural Networks Learns Functions of Increasing Complexity](https://arxiv.org/abs/1905.11604) NIPS 2019 (SGD learns a linear classifier first)
- [Pay attention to the activations: a modular attention mechanism for fine-grained image recognition](https://arxiv.org/abs/1907.13075)
- [A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images](https://arxiv.org/abs/1805.03225) BMVC 2018 (multi-bin, what's new?)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://arxiv.org/abs/1712.02616) CVPR 2018 (optimized BatchNorm + ReLU)
- [FCNN: Fourier Convolutional Neural Networks](http://ecmlpkdd2017.ijs.si/papers/paperID11.pdf) (FFT as CNN)
- [Visualizing the Loss Landscape of Neural Nets](https://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets.pdf) NIPS 2018
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/pdf/1610.02357.pdf) (Xception)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://arxiv.org/abs/1705.07115) (uncertainty)
- [Learning to Drive from Simulation without Real World Labels](https://arxiv.org/abs/1812.03823) ICRA 2019 (domain adaptation, sim2real)
- [Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks](https://arxiv.org/abs/1911.09737) CVPR 2020 oral
- [Switchable Whitening for Deep Representation Learning](https://arxiv.org/abs/1904.09739) ICCV 2019 [domain adaptation]
- [Visual Chirality](https://arxiv.org/abs/2006.09512) CVPR 2020 oral [best paper nominee]
- [Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data](https://arxiv.org/abs/2002.11297) CVPR 2020
- [Self-training with Noisy Student improves ImageNet classification](https://arxiv.org/abs/1911.04252) CVPR 2020 [distillation]
- [Keep it Simple: Image Statistics Matching for Domain Adaptation](https://arxiv.org/abs/2005.12551) CVPRW 2020 [Domain adaptation for 2D mod bbox]
- [Epipolar Transformers](https://arxiv.org/abs/2005.04551) CVPR 2020 [Yihui He]
- [Scalable Uncertainty for Computer Vision With Functional Variational Inference](https://arxiv.org/abs/2003.03396) CVPR 2020 [epistemic uncertainty with one fwd pass]
### Mono3D
- [3DOP: 3D Object Proposals for Accurate Object Class Detection](https://papers.nips.cc/paper/5644-3d-object-proposals-for-accurate-object-class-detection) NIPS 2015
- [DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation](https://arxiv.org/abs/1904.10097)
- [Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery](https://arxiv.org/abs/1808.06253) ECCV 2018 (Monocular 3D object detection and depth estimation)
- [Towards Scene Understanding: Unsupervised Monocular Depth Estimation with Semantic-aware Representation](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Towards_Scene_Understanding_Unsupervised_Monocular_Depth_Estimation_With_Semantic-Aware_Representation_CVPR_2019_paper.pdf) CVPR 2019 [unified conditional decoder]
- [DDP: Dense Depth Posterior from Single Image and Sparse Range](https://arxiv.org/abs/1901.10034) CVPR 2019
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://arxiv.org/abs/1708.01566) IJCV 2018 (data augmentation with AR, Toyota)
- [Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data](https://arxiv.org/abs/2005.07424) IITS
- [Towards Scene Understanding with Detailed 3D Object Representations](https://arxiv.org/abs/1411.5935) IJCV 2014 (keypoint, 3D bbox annotation)
- [Deep Cuboid Detection: Beyond 2D Bounding Boxes](https://arxiv.org/abs/1611.10010) (Magic Leap)
- [Viewpoints and Keypoints](https://arxiv.org/abs/1411.6067) (Malik)
- [Lifting Object Detection Datasets into 3D](https://arxiv.org/abs/1503.06465) (PASCAL)
- [3D Object Class Detection in the Wild](https://arxiv.org/abs/1503.05038) (keypoint based)
- [Fast Single Shot Detection and Pose Estimation](https://arxiv.org/abs/1609.05590) 3DV 2016 (SSD + pose, Wei Liu)
- [Virtual KITTI 2](https://arxiv.org/abs/2001.10773)
- [Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing](https://arxiv.org/abs/1612.02699) CVPR 2017
- [Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views](https://arxiv.org/abs/1505.05641) ICCV 2015 Oral
- [Real-Time Seamless Single Shot 6D Object Pose Prediction](https://arxiv.org/abs/1711.08848) CVPR 2018
- [Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching](https://arxiv.org/abs/1806.01677) NIPS 2018 [disparity estimation]
- [Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera](https://arxiv.org/abs/1807.00275) ICRA 2019
- [Learning Depth with Convolutional Spatial Propagation Network](https://arxiv.org/abs/1810.02695) (Baidu, depth from SPN) ECCV 2018
- [Just Go with the Flow: Self-Supervised Scene Flow Estimation](https://arxiv.org/abs/1912.00497) CVPR 2020 oral [Scene flow, Lidar]
- [Online Depth Learning against Forgetting in Monocular Videos](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Online_Depth_Learning_Against_Forgetting_in_Monocular_Videos_CVPR_2020_paper.pdf) CVPR 2020 [monodepth]
- [Self-Supervised Deep Visual Odometry with Online Adaptation](https://arxiv.org/abs/2005.06136) CVPR 2020 oral [DF-VO, TrianFlow, meta-learning]
- [Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume](https://arxiv.org/abs/2003.13951) CVPR 2020
- [Online Depth Learning against Forgetting in Monocular Videos](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Online_Depth_Learning_Against_Forgetting_in_Monocular_Videos_CVPR_2020_paper.pdf) CVPR 2020 [monodepth, online learning]
- [SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_SDC-Depth_Semantic_Divide-and-Conquer_Network_for_Monocular_Depth_Estimation_CVPR_2020_paper.pdf) CVPR 2020 [monodepth, semantic]
- [Inferring Distributions Over Depth from a Single Image](http://www.contrib.andrew.cmu.edu/~gengshay/wordpress/wp-content/uploads/2018/11/iros_monodepth_uncertainty.pdf) TRO [Depth confidence, stitching them together]
- [Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths](https://arxiv.org/abs/2004.01294) CVPR 2020
- [The Edge of Depth: Explicit Constraints between Segmentation and Depth](https://arxiv.org/abs/2004.00171) CVPR 2020 [Xiaoming Liu, multimodal, depth bleeding]
### Radar Perception
- [MV-RSS: Multi-View Radar Semantic Segmentation](https://arxiv.org/abs/2103.16214) ICCV 2021
- [Classification of Objects in Polarimetric Radar Images Using CNNs at 77 GHz](http://sci-hub.tw/10.1109/APMC.2017.8251453) (Radar, polar)
- [CNNs for Interference Mitigation and Denoising in Automotive Radar Using Real-World Data](https://ml4ad.github.io/files/papers/CNNs%20for%20Interference%20Mitigation%20and%20Denoising%20in%20Automotive%20Radar%20Using%20Real-World%20Data.pdf) NeurIPS 2019 (radar)
- [Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation](https://arxiv.org/abs/1904.00415) ICCV 2019 (radar)
- [RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects](https://arxiv.org/abs/2007.14366) ECCV 2020 [Uber ATG]
- [Depth Estimation from Monocular Images and Sparse Radar Data](https://arxiv.org/abs/2010.00058) IROS 2020 [Camera + Radar for monodepth, nuscenes]
- [RPR: Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles](https://arxiv.org/abs/2009.08428) IROS 2020 [radar proposal refinement]
- [Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications](https://arxiv.org/abs/2012.12809)
### SLAM
- [PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization](https://arxiv.org/abs/1505.07427) [[Notes](paper_notes/posenet.md)] ICCV 2015
- [PoseNet2: Modelling Uncertainty in Deep Learning for Camera Relocalization](https://arxiv.org/abs/1509.05909) ICRA 2016
- [PoseNet3: Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://arxiv.org/abs/1704.00390) CVPR 2017
- [EssNet: Convolutional neural network architecture for geometric matching](https://arxiv.org/abs/1703.05593) CVPR 2017
- [NC-EssNet: Neighbourhood Consensus Networks](https://arxiv.org/abs/1810.10510) NeurIPS 2018
- [Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task](https://arxiv.org/abs/1912.00623) CVPR 2020 oral [Eric Brachmann, ngransac]
- [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://arxiv.org/pdf/1802.05522.pdf) CVPR 2018
- [DynSLAM: Robust Dense Mapping for Large-Scale Dynamic Environments](https://siegedog.com/dynslam/) [dynamic SLAM, Andreas Geiger] ICRA 2018
- [GCNv2: Efficient Correspondence Prediction for Real-Time SLAM](https://arxiv.org/abs/1902.11046) LRA 2019 [Superpoint + orb slam]
- [Real-time Scalable Dense Surfel Mapping](Real-time Scalable Dense Surfel Mapping) ICRA 2019 [dense reconstruction, monodepth]
- [Dynamic SLAM: The Need For Speed](https://arxiv.org/abs/2002.08584)
- [GSLAM: A General SLAM Framework and Benchmark](https://arxiv.org/abs/1902.07995) ICCV 2019
### Radar Perception
- [Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar](https://openaccess.thecvf.com/content_CVPR_2020/papers/Scheiner_Seeing_Around_Street_Corners_Non-Line-of-Sight_Detection_and_Tracking_In-the-Wild_Using_CVPR_2020_paper.pdf) CVPR 2020 [Daimler]
- [Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous Vehicles](https://arxiv.org/abs/2008.13642) ICIP 2020
- [Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor](https://www.mdpi.com/1424-8220/20/4/956) sensors 2020 [radar, camera, early fusion]
### Reviews and Surveys
- [A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence](https://arxiv.org/abs/2006.12567)
- [Monocular Depth Estimation Based On Deep Learning: An Overview](https://arxiv.org/abs/2003.06620)
### Beyond Perception in Autonomous Driving
- [Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining](https://arxiv.org/abs/1906.11129) CVPR 2019
- [Learn to Combine Modalities in Multimodal Deep Learning](https://arxiv.org/abs/1805.11730) (sensor fusion, general DL)
- [Safe Trajectory Generation For Complex Urban Environments Using Spatio-temporal Semantic Corridor](https://arxiv.org/abs/1906.09788) LRA 2019 [Motion planning]
- [DAgger: Driving Policy Transfer via Modularity and Abstraction](https://arxiv.org/abs/1804.09364) CoRL 2018 [DAgger, Immitation Learning]
- [Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching](https://arxiv.org/abs/2003.02746) ICRA 2020 [Motion planning]
- [Calibration of Heterogeneous Sensor Systems](https://arxiv.org/pdf/1812.11445.pdf)
- [Intro：Sensor Fusion for Adas 无人驾驶中的数据融合 (from 知乎)](https://zhuanlan.zhihu.com/p/40967227) (Up to CVPR 2018)
- [YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving](https://arxiv.org/pdf/1904.05673.pdf) CVPR 2019 (Real Time, Low Power)
- [Deep Fusion of Heterogeneous Sensor Modalities for the Advancements of ADAS to Autonomous Vehicles](http://sci-hub.tw/10.1109/VLSI-DAT.2018.8373245)
- [Temporal Coherence for Active Learning in Videos](https://arxiv.org/abs/1908.11757) ICCVW 2019 [active learning, temporal coherence]
- [R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving](https://arxiv.org/abs/2011.06372) RTSS 2020 [perception system design]
### Prediction and Planning
- [Learning Lane Graph Representations for Motion Forecasting](https://arxiv.org/abs/2007.13732) ECCV 2020 [Uber ATG]
- [DSDNet: Deep Structured self-Driving Network](https://arxiv.org/abs/2008.06041) ECCV 2020 [Uber ATG]
### Annotation and Tooling
- [Temporal Coherence for Active Learning in Videos](https://arxiv.org/abs/1908.11757) ICCV 2019 workshop
- [Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation](https://arxiv.org/abs/1807.06072) ITSC 2018 [UToronto, autolabeling]
- [Learning Multi-Object Tracking and Segmentation From Automatic Annotations](http://openaccess.thecvf.com/content_CVPR_2020/html/Porzi_Learning_Multi-Object_Tracking_and_Segmentation_From_Automatic_Annotations_CVPR_2020_paper.html) CVPR 2020 [Autolabeling]
- [Canonical Surface Mapping via Geometric Cycle Consistency](https://arxiv.org/abs/1907.10043) ICCV 2019
- [TIDE: A General Toolbox for Identifying Object Detection Errors](https://arxiv.org/abs/2008.08115) ECCV 2018 [tools]
### Low level DL
- [Self-Supervised Camera Self-Calibration from Video](https://arxiv.org/abs/2112.03325) [TRI, intrinsic calibration, fisheye/pinhole]
### Early NLP papers
- [A Convolutional Neural Network for Modelling Sentences](https://arxiv.org/pdf/1404.2188.pdf) ACL 2014
- [FastText: Bag of Tricks for Efficient Text Classification](https://arxiv.org/pdf/1607.01759.pdf) ACL 2017
- [Siamese recurrent architectures for learning sentence similarity](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12195/12023) AAAI 2016
- [Efficient Estimation of Word Representations in Vector Space](https://arxiv.org/pdf/1301.3781.pdf) ICLR 2013
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://arxiv.org/pdf/1409.0473.pdf) ICLR 2015
- [Transformers: Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf) NIPS 2017
## Non-DL
- [Ad推荐系统方向文章汇总](https://github.com/wzhe06/Ad-papers)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://arxiv.org/abs/1802.03426) [[Notes](paper_notes/umap.md)] (dimension reduction, better than t-SNE)
## Technical Debt
- [Review Notes of Classical Key Points and Descriptors](paper_notes/classical_keypoints.md)
- CRF
- [Visual SLAM and Visual Odometry](https://link.springer.com/content/pdf/10.1007%2Fs40903-015-0032-7.pdf)
- ORB SLAM
- Bundle Adjustment
- 3D vision
- [SLAM/VIO学习总结](https://zhuanlan.zhihu.com/p/34995102)
- [Design Patterns](https://refactoring.guru/design-patterns/python)
## To be organized (CVPR 2021 and ICCV 2021 the pile to be read)
- [Capturing Omni-Range Context for Omnidirectional Segmentation](https://arxiv.org/abs/2103.05687) CVPR 2021
- [UP-DETR: Unsupervised Pre-training for Object Detection with Transformers](https://arxiv.org/abs/2011.09094) CVPR 2021 [transformers]
- [DCL: Dense Label Encoding for Boundary Discontinuity Free Rotation Detection](https://arxiv.org/abs/2011.09670) CVPR 2021
- [4D Panoptic LiDAR Segmentation](https://arxiv.org/abs/2102.12472) CVPR 2021 [TUM]
- [CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild](https://arxiv.org/abs/2011.14679) CVPR 2021
- [Fast and Accurate Model Scaling](https://arxiv.org/abs/2103.06877) CVPR 2021 [FAIR]
- [Cylinder3D: Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://arxiv.org/abs/2011.10033) CVPR 2021 [lidar semantic segmentation]
- [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://arxiv.org/abs/2103.15297) CVPR 2021 [TuSimple, Lidar]
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://arxiv.org/abs/2011.13005) CVPR 2021 oral
- [DBB: Diverse Branch Block: Building a Convolution as an Inception-like Unit](https://arxiv.org/abs/2103.13425) CVPR 2021 [RepVGG, ACNet, Xiaohan Ding, Megvii]
- [GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection](https://arxiv.org/abs/2103.17202) CVPR 2021 [mono3D]
- [DDMP: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection](https://arxiv.org/abs/2103.16470) CVPR 2021 [mono3D]
- [M3DSSD: Monocular 3D Single Stage Object Detector](https://arxiv.org/abs/2103.13164) CVPR 2021 [mono3D]
- [MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation](https://arxiv.org/abs/2103.12605) CVPR 2021 [mono3D]
- [HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection](https://arxiv.org/abs/2104.00902) CVPR 2021 [Lidar]
- [PLUME: Efficient 3D Object Detection from Stereo Images](https://arxiv.org/abs/2101.06594) [Yan Wang, Uber ATG]
- [V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection](https://arxiv.org/abs/2104.03106) [crowded, pedestrian, megvii]
- [IP-basic: In Defense of Classical Image Processing: Fast Depth Completion on the CPU](https://arxiv.org/abs/1802.00036) CRV 2018
- [Revisiting Feature Alignment for One-stage Object Detection](https://arxiv.org/abs/1908.01570) [cls+reg]
- [Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment](https://arxiv.org/abs/2009.08650) WACV 2021 [SafetyNet]
- [TSD: Revisiting the Sibling Head in Object Detector](https://arxiv.org/abs/2003.07540) CVPR 2020 [sensetime, cls+reg]
- [1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation](https://arxiv.org/abs/2003.07557) [sensetime, cls+reg, 1st place OpenImage2019]
- [Enabling spatio-temporal aggregation in Birds-Eye-View Vehicle Estimation](https://cvssp.org/Personal/OscarMendez/papers/pdf/SahaICRA2021.pdf) ICRA 2021
- [End-to-end Lane Detection through Differentiable Least-Squares Fitting](https://arxiv.org/abs/1902.00293) ICCV workshop 2019
- [Revisiting ResNets: Improved Training and Scaling Strategies](https://arxiv.org/abs/2103.07579)
- [Multi-Modality Cut and Paste for 3D Object Detection](https://arxiv.org/abs/2012.12741)
- [LD: Localization Distillation for Object Detection](https://arxiv.org/abs/2102.12252)
- [PolyTransform: Deep Polygon Transformer for Instance Segmentation](https://arxiv.org/abs/1912.02801) CVPR 2020 [single stage instance segmentation]
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://arxiv.org/abs/2102.11585)
- [LidarMTL: A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding](https://arxiv.org/abs/2103.04056) [lidar MTL]
- [High-Performance Large-Scale Image Recognition Without Normalization](https://arxiv.org/abs/2102.06171) ICLR 2021
- [Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://arxiv.org/abs/2102.00690) RA-L [mono3D]
- [Demystifying Pseudo-LiDAR for Monocular 3D Object Detection](https://arxiv.org/abs/2012.05796) [mono3d]
- [Pseudo-labeling for Scalable 3D Object Detection](https://arxiv.org/abs/2103.02093) [Waymo]
- [LLA: Loss-aware Label Assignment for Dense Pedestrian Detection](https://arxiv.org/abs/2101.04307) [Megvii]
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://arxiv.org/abs/2005.04259) CVPR 2020 [Waymo]
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://arxiv.org/abs/1911.10298) CVPR 2020 [prediction, nuScenes]
- [SplitNet: Divide and Co-training](https://arxiv.org/abs/2011.14660)
- [VoVNet: An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://arxiv.org/abs/1904.09730) CVPR 2019 workshop
- [Isometric Neural Networks: Non-discriminative data or weak model? On the relative importance of data and model resolution](https://arxiv.org/abs/1909.03205) ICCV 2019 workshop [spatial2channel]
- [TResNet](https://arxiv.org/abs/2003.13630) WACV 2021 [spatial2channel]
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://arxiv.org/abs/1911.08287) AAAI 2020 [DIOU, NMS]
- [RegNet: Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) CVPR 2020 [FAIR]
- [On Network Design Spaces for Visual Recognition](https://arxiv.org/abs/1905.13214) [FAIR]
- [Lane Endpoint Detection and Position Accuracy Evaluation for Sensor Fusion-Based Vehicle Localization on Highways](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308985/) Sensors 2018 [lane endpoints]
- [Map-Matching-Based Cascade Landmark Detection and Vehicle Localization](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8826538) IEEE Access 2019 [lane endpoints]
- [GCNet: End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://arxiv.org/abs/1703.04309) ICCV 2017 [disparity estimation, Alex Kendall, cost volume]
- [Traffic Control Gesture Recognition for Autonomous Vehicles](https://arxiv.org/abs/2007.16072) IROS 2020 [Daimler]
- [Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild](https://arxiv.org/abs/2007.15649) ECCV 2020
- [OrcVIO: Object residual constrained Visual-Inertial Odometry](https://arxiv.org/abs/2007.15107) [dynamic SLAM, very mathematical]
- [InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling](https://arxiv.org/abs/2007.08556) ECCV 2020
- [DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving](https://arxiv.org/abs/2003.03026) ECCV 2020
- [Towards Lightweight Lane Detection by Optimizing Spatial Embedding](https://arxiv.org/abs/2008.08311) ECCV 2020 workshop [LLD]
- [Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection](https://arxiv.org/abs/2009.11859) ECCV 2020 workshop [lidar]
- [DeepIM: Deep iterative matching for 6d pose estimation](https://openaccess.thecvf.com/content_ECCV_2018/papers/Yi_Li_DeepIM_Deep_Iterative_ECCV_2018_paper.pdf) ECCV 2018 [pose estimation]
- [Monocular Depth Prediction through Continuous 3D Loss](https://arxiv.org/abs/2003.09763) IROS 2020
- [Multi-Task Learning for Dense Prediction Tasks: A Survey](https://arxiv.org/abs/2004.13379) [MTL, Luc Van Gool]
- [Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems](https://arxiv.org/abs/2001.02223) ITSC 2020 oral [MTL]
- [NeurAll: Towards a Unified Model for Visual Perception in Automated Driving](https://arxiv.org/abs/1902.03589) ITSC 2019 oral [MTL]
- [Deep Evidential Regression](https://papers.nips.cc/paper/2020/file/aab085461de182608ee9f607f3f7d18f-Paper.pdf) NeurIPS 2020 [one-pass aleatoric/epistemic uncertainty]
- [Estimating Drivable Collision-Free Space from Monocular Video](http://www.cs.toronto.edu/~yaojian/freeSpace.pdf) WACV 2015 [Drivable space]
- [Visualization of Convolutional Neural Networks for Monocular Depth Estimation](https://arxiv.org/abs/1904.03380) ICCV 2019 [monodepth]
- [Differentiable Rendering: A Survey](https://arxiv.org/abs/2006.12057) [differentiable rendering, TRI]
- [SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware
Feature Extraction](https://arxiv.org/abs/2010.02893) [monodepth, semantics, Naver labs]
- [Toward Interactive Self-Annotation For Video Object Bounding Box: Recurrent Self-Learning And Hierarchical Annotation Based Framework](https://openaccess.thecvf.com/content_WACV_2020/papers/Le_Toward_Interactive_Self-Annotation_For_Video_Object_Bounding_Box_Recurrent_Self-Learning_WACV_2020_paper.pdf) WACV 2020
- [Towards Good Practice for CNN-Based Monocular Depth Estimation](http://openaccess.thecvf.com/content_WACV_2020/papers/Fang_Towards_Good_Practice_for_CNN-Based_Monocular_Depth_Estimation_WACV_2020_paper.pdf) WACV 2020
- [Self-Supervised Scene De-occlusion](https://arxiv.org/abs/2004.02788) CVPR 2020 oral
- [TP-LSD: Tri-Points Based Line Segment Detector](https://arxiv.org/abs/2009.05505)
- [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440) CVPR 2018 [Kaiming He, FAIR]
- [MiDas: Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://arxiv.org/abs/1907.01341) [monodepth, dynamic object, synthetic dataset]
- [Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation](https://arxiv.org/abs/2006.04371) [monodepth]
- [Towards Lightweight Lane Detection by Optimizing Spatial Embedding](https://arxiv.org/abs/2008.08311) ECCV 2020 workshop
- [Synthetic-to-Real Domain Adaptation for Lane Detection](https://arxiv.org/abs/2007.04023) [GM Israel, LLD]
- [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://arxiv.org/abs/2004.10924) ICPR 2020 [polynomial, LLD]
- [Learning Universal Shape Dictionary for Realtime Instance Segmentation](https://arxiv.org/abs/2012.01050)
- [End-to-End Video Instance Segmentation with Transformers](https://arxiv.org/abs/2011.14503) [DETR, transformers]
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://arxiv.org/abs/1910.01279) CVPR 2020 workshop
- [When and Why Test-Time Augmentation Works](https://arxiv.org/abs/2011.11156)
- [Footprints and Free Space from a Single Color Image](https://arxiv.org/abs/2004.06376) CVPR 2020 oral [Parking use, footprint]
- [Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning](https://arxiv.org/abs/2008.04047) [BEV, only predict footprint]
- [Rethinking Classification and Localization for Object Detection](https://arxiv.org/abs/1904.06493) CVPR 2020
- [Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation](https://arxiv.org/abs/2011.14589) [mono3D]
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://arxiv.org/abs/2012.07177)
- [ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation](https://arxiv.org/abs/2012.05258)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://arxiv.org/abs/1804.02505) ECCV 2018
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://arxiv.org/abs/1902.10556) CVPR 2019 [Deep learning + MVS, Vidar, same author MVSNet]
- [Artificial Dummies for Urban Dataset Augmentation](https://arxiv.org/abs/2012.08274) AAAI 2021
- [DETR for Pedestrian Detection](https://arxiv.org/abs/2012.06785) [transformer, pedestrian detection]
- [Multi-Modality Cut and Paste for 3D Object Detection](https://arxiv.org/abs/2012.12741) [SenseTime]
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://arxiv.org/abs/2012.15840) [transformer, semantic segmenatation]
- [TransPose: Towards Explainable Human Pose Estimation by Transformer](https://arxiv.org/abs/2012.14214) [transformer, pose estimation]
- [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032)
- [SWA Object Detection](https://arxiv.org/abs/2012.12645) [Stochastic Weights Averaging (SWA)]
- [3D Object Detection with Pointformer](https://arxiv.org/abs/2012.11409)
- [Toward Transformer-Based Object Detection](https://arxiv.org/abs/2012.09958) [DETR-like]
- [Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion](https://arxiv.org/abs/2012.10296) [dense SfM]
- [Multi-Modality Cut and Paste for 3D Object Detection](https://arxiv.org/abs/2012.12741)
- [Vision Global Localization with Semantic Segmentation and Interest Feature Points](http://ras.papercept.net/images/temp/IROS/files/1899.pdf)
- [Transformer Interpretability Beyond Attention Visualization](https://arxiv.org/abs/2012.09838) [transformers]
- [Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU](https://arxiv.org/abs/2012.07489)
- [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/abs/2006.02334)
- [Empirical Upper Bound in Object Detection and More](https://arxiv.org/abs/1911.12451)
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://arxiv.org/abs/2012.02124) [Fisheye, Senthil Yogamani]
- [Monocular 3D Object Detection with Sequential Feature Association and Depth Hint Augmentation](https://arxiv.org/abs/2011.14589) [mono3D]
- [SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images](http://arxiv.org/abs/2101.07422) [Jiwen Lu, monodepth]
- [Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion](https://arxiv.org/abs/2103.16690) [TRI]
- [Linformer: Self-Attention with Linear Complexity](https://arxiv.org/abs/2006.04768)
- [Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks](https://arxiv.org/abs/1810.00825) ICML 2019
- [PCT: Point cloud transformer](https://arxiv.org/abs/2012.09688) Computational Visual Media 2021
- [DDT: Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming](https://arxiv.org/abs/1707.06397) IJCAI 2017
- [Hierarchical Road Topology Learning for Urban Map-less Driving](https://arxiv.org/abs/2104.00084) [Mercedes]
- [Probabilistic Future Prediction for Video Scene Understanding](https://arxiv.org/abs/2003.06409) ECCV 2020 [Alex Kendall]
- [Detecting 32 Pedestrian Attributes for Autonomous Vehicles](https://arxiv.org/abs/2012.02647) [VRU, MTL]
- [Cascaded deep monocular 3D human pose estimation with evolutionary training data](https://arxiv.org/abs/2006.07778) CVPR 2020 oral
- [MonoGeo: Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection](https://arxiv.org/abs/2107.13931) [mono3D]
- [Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth](https://arxiv.org/abs/2107.13269) [mono3D]
- [Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting](https://arxiv.org/abs/2107.02493) [mono3D]
- [Lite-FPN for Keypoint-based Monocular 3D Object Detection](https://arxiv.org/abs/2105.00268) [mono3D]
- [Lidar Point Cloud Guided Monocular 3D Object Detection](https://arxiv.org/abs/2104.09035)
- [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) [Vladlen Koltun, Intel]
- [Efficient Transformers: A Survey](https://arxiv.org/abs/2009.06732)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://arxiv.org/abs/2108.08810)
- [Progressive Coordinate Transforms for Monocular 3D Object Detection](http://arxiv.org/abs/2108.05793) [mono3D]
- [AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection](https://arxiv.org/abs/2108.11127) ICCV 2021 [mono3D]
- [BlazePose: On-device Real-time Body Pose tracking](https://arxiv.org/abs/2006.10204)
## TODO
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://arxiv.org/abs/2204.00598) [Andy Zeng]
- [Large Language Models as General Pattern Machines](https://arxiv.org/abs/2307.04721) [Embodied AI]
- RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
- [PlaNet: Learning Latent Dynamics for Planning from Pixels](https://arxiv.org/abs/1811.04551) ICML 2019
- [Dreamer: Dream to Control: Learning Behaviors by Latent Imagination](https://arxiv.org/abs/1912.01603) ICLR 2020 oral
- [DreamerV2: Mastering Atari with Discrete World Models](https://arxiv.org/abs/2010.02193) ICLR 2021 [World models]
- [DreamerV3: Mastering Diverse Domains through World Models](https://arxiv.org/abs/2301.04104)
- [DayDreamer: World Models for Physical Robot Learning](https://arxiv.org/abs/2206.14176) CoRL 2022
- [JEPA: A Path Towards Autonomous Machine Intelligence](https://openreview.net/pdf?id=BZ5a1r-kVsf)
- [I-JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://arxiv.org/abs/2301.08243) CVPR 2023
- [Runway Gen-1: Structure and Content-Guided Video Synthesis with Diffusion Models](https://arxiv.org/abs/2302.03011)
- [IL Difficulty Model: Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula](https://arxiv.org/abs/2212.01375) CoRL 2022 [Waymo]
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) NeurIPS 2021 [LLM for planning]
- [LID: Pre-Trained Language Models for Interactive Decision-Making](https://arxiv.org/abs/2202.01771) NeurIPS 2022 [LLM for planning]
- [Planning with Large Language Models via Corrective Re-prompting](https://arxiv.org/abs/2211.09935) NeurIPS 2022 Workshop
- [Object as Query: Equipping Any 2D Object Detector with 3D Detection Ability](https://arxiv.org/abs/2301.02364) ICCV 2023 [TuSimple]
- [Speculative Sampling: Accelerating Large Language Model Decoding with Speculative Sampling](https://arxiv.org/abs/2302.01318) [Accelerated LLM, DeepMind]
- [Inference with Reference: Lossless Acceleration of Large Language Models](https://arxiv.org/abs/2304.04487) [Accelerated LLM, Microsoft]
- [EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments](https://arxiv.org/abs/2108.07993) T-RO 2021
- [Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching](https://arxiv.org/abs/2003.02746) ICRA 2020
- [StreamPETR: Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection](https://arxiv.org/abs/2303.11926)
- [SSCNet: Semantic Scene Completion from a Single Depth Image](https://arxiv.org/abs/1611.08974) CVPR 2017
- [SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences](https://arxiv.org/abs/1904.01416) ICCV 2019
- [PixPro: Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning](https://arxiv.org/abs/2011.10043) [self-supervised]
- [Pixel-Wise Contrastive Distillation](https://arxiv.org/abs/2211.00218) [self-supervised]
- [VICRegL: Self-Supervised Learning of Local Visual Features](https://arxiv.org/abs/2210.01571) NeurIPS 2022
- [ImageBind: One Embedding Space To Bind Them All](https://arxiv.org/abs/2305.05665) CVPR 2023
- [KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long-Term Trajectory Prediction](https://arxiv.org/abs/2205.04624) ICRA 2022 [Planning]
- [Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models](https://arxiv.org/abs/2204.02392) L4DC [Planning]
- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://arxiv.org/abs/2303.05760) [Planning]
- [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://arxiv.org/abs/2101.06547) [Planning, Raquel]
- [DIPP: Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://arxiv.org/abs/2207.10422) [Planning]
- [Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios](https://arxiv.org/abs/2212.11419) [Planning, Waymo]
- [Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving](https://arxiv.org/abs/2210.09539) IROS 2022 [Planning, Waymo]
- [Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation](https://arxiv.org/abs/2205.03195) ICRA 2022 [Planning, Waymo]
- [JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving](https://arxiv.org/abs/2212.08710) [Planning, Waymo]
- [MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) NeurIPS 2021
- [3D Semantic Scene Completion: a Survey](https://arxiv.org/abs/2103.07466) IJCV 2022
- [DETIC: Detecting Twenty-thousand Classes using Image-level Supervision](https://arxiv.org/abs/2201.02605) ECCV 2022
- [Atlas: End-to-End 3D Scene Reconstruction from Posed Images](https://github.com/magicleap/Atlas) ECCV 2020
- [TransformerFusion: Monocular RGB Scene Reconstruction using Transformers](https://arxiv.org/abs/2107.02191) NeurIPS 2021
- [SimpleOccupancy: A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving](https://arxiv.org/abs/2303.10076) [Occupancy Network]
- [OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion](https://arxiv.org/abs/2302.13540) [Occupancy Network, stereo]
- [Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception](https://arxiv.org/abs/2301.07870) NeurIPS 2022
- [Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline](https://arxiv.org/abs/2301.12511)
- [ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals](https://arxiv.org/abs/2303.12071) CVPR 2023 [Qcraft, prediction]
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://arxiv.org/abs/2209.13508) NeurIPS 2022 Oral
- [P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving](https://arxiv.org/abs/2211.01634)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://arxiv.org/abs/2111.14973)
- [ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries](https://arxiv.org/abs/2208.01582)
- [SAM: Segment Anything](https://arxiv.org/abs/2304.02643) [FAIR]
- [GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding](https://arxiv.org/abs/2303.11325)
- [Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge](https://arxiv.org/abs/2006.04767) [Encode Road requirement to prediction]
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://arxiv.org/abs/2012.14913) EMNLP 2021
- [BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline](https://arxiv.org/abs/2210.06006) CVPR 2023 [BEVNet]
- [Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception](https://arxiv.org/abs/2303.05970) [BEVNet, megvii]
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://arxiv.org/abs/2303.12077) [Horizon]
- [A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving](https://arxiv.org/abs/2303.10076)
- [BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment](https://arxiv.org/abs/2211.17111) [BEVDet, PhiGent]
- [NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving](http://aixpaper.com/view/nvradarnet_realtime_radar_obstacle_and_free_space_detection_for_autonomous_driving)
- [GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_GraspNet-1Billion_A_Large-Scale_Benchmark_for_General_Object_Grasping_CVPR_2020_paper.pdf) CVPR 2020 [Cewu Lu]
- [AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains](https://arxiv.org/abs/2212.08333) [Cewu Lu]
- [Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting](https://arxiv.org/abs/2302.13130)
- [HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding](https://arxiv.org/abs/2205.09753)
- [MTR: Motion Transformer with Global Intention Localization and Local Movement Refinement](https://arxiv.org/abs/2209.13508) NeurIPS 2022
- [UVTR: Unifying Voxel-based Representation with Transformer for 3D Object Detection](https://arxiv.org/abs/2206.00630) [BEVFusion, Megvii, BEVNet, camera + lidar]
- [Don't Use Large Mini-Batches, Use Local SGD](https://arxiv.org/abs/1808.07217) ICLR 2020
- [Grokking: Generalization beyond Overfitting on small algorithmic datasets](https://arxiv.org/abs/2201.02177)
- [Progress measures for grokking via mechanistic interpretability]()
- [Understanding deep learning requires rethinking generalization](https://arxiv.org/abs/1611.03530) ICLR 2017
- [Unifying Grokking and Double Descent](https://openreview.net/forum?id=JqtHMZtqWm)
- [Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models](https://arxiv.org/abs/2204.02392) L4DC 2022
- [Interactive Prediction and Planning for Autonomous Driving: from Algorithms to Fundamental Aspects](https://escholarship.org/uc/item/0vf4q2x1) [PhD thesis of Wei Zhan, 2019]
- [Lyft1001: One Thousand and One Hours: Self-driving Motion Prediction Dataset](https://arxiv.org/abs/2006.14480) [Lyft Level 5, prediction dataset]
- [PCAccumulation: Dynamic 3D Scene Analysis by Point Cloud Accumulation](https://arxiv.org/abs/2207.12394) ECCV 2022
- [UniSim: A Neural Closed-Loop Sensor Simulator](https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_UniSim_A_Neural_Closed-Loop_Sensor_Simulator_CVPR_2023_paper.pdf) CVPR 2023 [simulation, Raquel]
- [GeoSim: Realistic Video Simulation via Geometry-Aware Composition for
Self-Driving](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_GeoSim_Realistic_Video_Simulation_via_Geometry-Aware_Composition_for_Self-Driving_CVPR_2021_paper.pdf) CVPR 2023
- [Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills](https://arxiv.org/abs/2209.12072) [Driving Skill]
- [Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors](https://arxiv.org/abs/2305.04412) RSS 2023 [Driving Skill]
- [IL Difficulty Model: Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula](https://arxiv.org/abs/2212.01375) CoRL 2022 [Waymo]
- [Neural Map Prior for Autonomous Driving](https://arxiv.org/abs/2304.08481) CVPR 2023
- [Track Anything: Segment Anything Meets Videos](https://arxiv.org/abs/2304.11968)
- [Self-Supervised Camera Self-Calibration from Video](https://arxiv.org/abs/2112.03325) ICRA 2022 [TRI, calibration]
- [Real-time Online Video Detection with Temporal Smoothing Transformers](https://arxiv.org/abs/2209.09236) ECCV 2022 [ConvLSTM-style cross-attention]
- [NeRF-Supervised Deep Stereo](https://arxiv.org/abs/2303.17603) CVPR 2023
- [GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://arxiv.org/abs/2209.11163) NeurIOS 2022
- [OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation](https://arxiv.org/abs/2301.07525) CVPR 2023
- [Ego-Body Pose Estimation via Ego-Head Pose Estimation](https://arxiv.org/abs/2212.04636) CVPR 2023
- [PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation](https://arxiv.org/abs/2306.10013)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597)
- [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485)
- [VideoChat: Chat-Centric Video Understanding](https://arxiv.org/abs/2305.06355)
- [CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers](https://arxiv.org/abs/2207.02202) CoRL 2022
- [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://arxiv.org/abs/2211.10439) [BEVNet, Jifeng Dai]
- [Fast-BEV: Towards Real-time On-vehicle Bird’s-Eye View Perception](https://ml4ad.github.io/files/papers2022/Fast-BEV:%20Towards%20Real-time%20On-vehicle%20Bird's-Eye%20View%20Perception.pdf) NeurIPS 2022
- [Traj++: Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://arxiv.org/abs/2007.03639) TITS 2021
- [Data Driven Prediction Architecture for Autonomous Driving and its Application on Apollo Platform](https://arxiv.org/abs/2006.06715) IV 2020 [Baidu]
- [THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling](https://arxiv.org/abs/2110.06607) ICLR 2022
- [Learning Lane Graph Representations for Motion Forecasting](https://arxiv.org/abs/2007.13732) ECCV 2020 oral
- [Identifying Driver Interactions via Conditional Behavior Prediction](https://arxiv.org/abs/2104.09959) ICRA 2021 [Waymo]
- [Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data](https://arxiv.org/abs/2001.03093) ECCV 2020
- [TPNet: Trajectory Proposal Network for Motion Prediction](https://arxiv.org/abs/2004.12255) CVPR 2020
- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://arxiv.org/abs/2109.01827)
- [PECNet: It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://arxiv.org/abs/2004.02025) ECCV 2020 oral
- [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://arxiv.org/abs/2012.01526) ICCV 2019
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://arxiv.org/abs/1905.01296) ICCV 2019
- [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://arxiv.org/abs/2003.11476) ECCV 2020
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://arxiv.org/abs/1910.05449) CoRL 2019
- [LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents](https://arxiv.org/abs/2104.00249) CVPR 2021
- [PRIME: Learning to Predict Vehicle Trajectories with Model-based Planning](https://arxiv.org/abs/2103.04027) CoRL 2021
- [A Flexible and Explainable Vehicle Motion Prediction and Inference Framework Combining Semi-Supervised AOG and ST-LSTM](https://dl.acm.org/doi/abs/10.1109/TITS.2020.3016304) TITS 2020
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://arxiv.org/abs/1805.05499) IV 2018 [Trivedi]
- [HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling](https://arxiv.org/abs/2110.02344) ICRA 2022
- [Trajectory Prediction with Linguistic Representations](https://arxiv.org/abs/2110.09741) ICRA 2022
- [What-If Motion Prediction for Autonomous Driving](https://arxiv.org/abs/2008.10587)
- [End-to-end Contextual Perception and Prediction with Interaction Transformer](https://arxiv.org/abs/2008.05927) IROS 2020 [Auxiliary collision loss, scene compliant pred]
- [SafeCritic: Collision-Aware Trajectory Prediction](https://arxiv.org/abs/1910.06673) BMVC 2019 [IRL, scene compliant pred]
- [Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset](https://arxiv.org/abs/2104.10133) ICCV 2021 [Waymo]
- [Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph](https://arxiv.org/abs/2009.12916) IROS 2020
- [Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks](https://arxiv.org/abs/1912.07882) NeurIPS 2019 workshop
- [Fast Risk Assessment for Autonomous Vehicles Using Learned Models of Agent Futures](https://arxiv.org/abs/2005.13458) Robotics: science and systems 2020
- [Monocular 3D Object Detection: An Extrinsic Parameter Free Approach](https://arxiv.org/abs/2106.15796) CVPR 2021 [PJLab]
- [UniFormer: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View](https://arxiv.org/abs/2207.08536) [BEVFormer, BEVNet, Temporal]
- [GitNet: geometric prior-baesd transformation for birds yee view segmentation]()
- [WBF: weighted box fusion: ensembling boxes from differnt object detection modules]()
- [NNI: auto parameter finding algorithm]()
- [BEVFormer++: Improving BEVFormer for 3D Camera-only Object Detection](https://storage.googleapis.com/waymo-uploads/files/research/3DCam/3DCam_BEVFormer.pdf) [Waymo open dataset challenge 1st place in mono3d]
- [LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection](https://arxiv.org/abs/2206.07705) [Waymo open dataset challenge official metric]
- [High-Level Interpretation of Urban Road Maps Fusing Deep Learning-Based Pixelwise Scene Segmentation and Digital Navigation Maps](https://pdfs.semanticscholar.org/44ac/01c0d356f22e7ee883f8e4ac2cccf199f68d.pdf) Journal of Advanced Transportation 2018
- [A Hybrid Vision-Map Method for Urban Road Detection](https://downloads.hindawi.com/journals/jat/2017/7090549.pdf) Journal of Advanced Transportation 2017
- [Terminology and Analysis of Map Deviations in Urban Domains: Towards Dependability for HD Maps in Automated Vehicles](https://www.researchgate.net/profile/Christopher-Plachetka/publication/348367176_Terminology_and_Analysis_of_Map_Deviations_in_Urban_Domains_Towards_Dependability_for_HD_Maps_in_Automated_Vehicles/links/607d523f907dcf667babc06b/Terminology-and-Analysis-of-Map-Deviations-in-Urban-Domains-Towards-Dependability-for-HD-Maps-in-Automated-Vehicles.pdf) IV 2020
- [TIME WILL TELL: NEW OUTLOOKS AND A BASELINE FOR TEMPORAL MULTI-VIEW 3D OBJECT DETECTION](https://arxiv.org/abs/2210.02443)
- [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) ICCV 2021
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://arxiv.org/abs/2201.12329) ICLR 2022
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://arxiv.org/abs/2203.01305) CVPR 2022
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://arxiv.org/abs/2203.03605)
- [Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding](https://arxiv.org/abs/2202.01478) [Ouyang Wanli]
- [Vision-based Uneven BEV Representation Learning with Polar Rasterization and Surface Estimation](https://arxiv.org/abs/2207.01878) [BEVNet, polar]
- [MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries](https://arxiv.org/abs/2205.00613) [BEVNet, tracking] CVPR 2022 workshop [Hang Zhao]
- [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://arxiv.org/abs/2207.07601) ECCV 2022 [Hongyang Li]
- [GKT: Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel Transformer](https://arxiv.org/abs/2206.04584) [BEVNet, Horizon]
- [SiamRPN: High Performance Visual Tracking with Siamese Region Proposal Network](https://yan-junjie.github.io/publication/dblp-confcvpr-li-ywzh-18/dblp-confcvpr-li-ywzh-18.pdf) CVPR 2018
- [TPLR: Topology Preserving Local Road Network Estimation from Single Onboard Camera Image](https://arxiv.org/abs/2112.10155) CVPR 2022 [STSU, Luc Van Gool]
- [LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation](https://arxiv.org/abs/2206.13294) [Valeo, BEVNet, polar]
- [PolarDETR: Polar Parametrization for Vision-based Surround-View 3D Detection](https://arxiv.org/abs/2206.10965) [BEVNet]
- [Exploring Geometric Consistency for Monocular 3D Object Detection](https://arxiv.org/abs/2104.05858) CVPR 2022
- [ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection](https://arxiv.org/abs/2106.01178) WACV 2022 [mono3D]
- [Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints](https://arxiv.org/abs/2112.15351) AAAI 2022
- [Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers](https://ieeexplore.ieee.org/document/9294383) ICICN 2021 [BEVNet, lane line]
- [Unsupervised Labeled Lane Markers Using Maps](https://openaccess.thecvf.com/content_ICCVW_2019/papers/CVRSUAD/Behrendt_Unsupervised_Labeled_Lane_Markers_Using_Maps_ICCVW_2019_paper.pdf) ICCV 2019 workshop [Bosch, 2D lane line]
- [M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers](https://arxiv.org/abs/2104.11896) [Lidar detection, Waymo open dataset] WACV 2022
- [K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways](https://arxiv.org/abs/2110.11048) [lane line dataset]
- [Robust Monocular 3D Lane Detection With Dual Attention](https://ieeexplore.ieee.org/abstract/document/9506296) ICIP 2021
- [OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction](https://arxiv.org/abs/2203.07977) CVPR 2022
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) ICLR 2022 [lightweight Transformers]
- [XFormer: Lightweight Vision Transformer with Cross Feature Attention](https://arxiv.org/abs/2207.07268) [Samsung]
- [CenterFormer: Center-based Transformer for 3D Object Detection](https://arxiv.org/abs/2209.05588) ECCV 2022 oral [TuSimple]
- [LidarMultiNet: Towards a Unified Multi-task Network for LiDAR Perception](https://arxiv.org/abs/2209.09385) [2022 Waymo Open Dataset, TuSimple]
- [MTRA: 1st Place Solution for 2022 Waymo Open Dataset Challenge - Motion Prediction](https://storage.googleapis.com/waymo-uploads/files/research/MotionPred/MotionPrediction_MTRA.pdf) [Waymo open dataset challenge 1st place in motion prediction]
- [BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs](https://arxiv.org/abs/2203.04050) [BEVNet]
- [Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers](https://arxiv.org/abs/2109.03814) CVPR 2022 [nVidia]
- [Efficiently Identifying Task Groupings for Multi-Task Learning](https://arxiv.org/abs/2109.04617) NeurIPS 2021 spotlight [MTL]
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://arxiv.org/abs/2203.05482) [Google, Golden Backbone]
- ["The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping](https://arxiv.org/abs/2204.02944) CVPR 2022
- [GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation](https://arxiv.org/abs/2204.07733) [BEVNet, Baidu]
- [FUTR3D: A Unified Sensor Fusion Framework for 3D Detection](https://arxiv.org/abs/2203.10642) [Hang Zhao]
- [GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation](https://arxiv.org/abs/2204.07733) [BEVNet]
- [MonoFormer: Towards Generalization of self-supervised monocular depth estimation with Transformers](https://arxiv.org/abs/2205.11083) [monodepth]
- [Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving](https://arxiv.org/abs/2205.14882)
- [cosFormer: Rethinking Softmax in Attention](https://arxiv.org/abs/2202.08791) ICLR 2022
- [StretchBEV: Stretching Future Instance Prediction Spatially and Temporally](https://arxiv.org/abs/2203.13641) [BEVNet, prediction]
- [Scene Representation in Bird’s-Eye View from Surrounding Cameras with Transformers](https://openaccess.thecvf.com/content/CVPR2022W/WAD/papers/Zhao_Scene_Representation_in_Birds-Eye_View_From_Surrounding_Cameras_With_Transformers_CVPRW_2022_paper.pdf) [BEVNet, LLD] CVPR 2022 workshop
- [Multi-Frame Self-Supervised Depth with Transformers](https://arxiv.org/abs/2204.07616) CVPR 2022
- [It's About Time: Analog Clock Reading in the Wild](https://arxiv.org/abs/2111.09162) CVPR 2022 [Andrew Zisserman]
- [SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation](https://arxiv.org/abs/2204.03636) CoRL 2022 [Jiwen Lu]
- [ONCE-3DLanes: Building Monocular 3D Lane Detection](https://arxiv.org/abs/2205.00301) CVPR 2022
- [K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways](https://arxiv.org/abs/2110.11048) CVPR 2022 workshop [3D LLD]
- [Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving](https://arxiv.org/abs/2112.12141) CVPR 2022 workshop
- [A Simple Baseline for BEV Perception Without LiDAR](https://arxiv.org/abs/2206.07959) [TRI, BEVNet, vision+radar]
- [Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior](https://openaccess.thecvf.com/content/CVPR2022W/WAD/papers/Li_Reconstruct_From_Top_View_A_3D_Lane_Detection_Approach_Based_CVPRW_2022_paper.pdf) CVPR 2022 workshop
- [RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding](https://arxiv.org/abs/2206.01738) CVPR 2022 [Waymo, Charles Qi]
- [Occupancy Flow Fields for Motion Forecasting in Autonomous Driving](https://arxiv.org/abs/2203.03875) RAL 2022 [Waymo occupancy flow challenge]
- [Safe Local Motion Planning with Self-Supervised Freespace Forecasting](https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.pdf) CVPR 2021
- [数据闭环的核心 - Auto-labeling 方案分享](https://zhuanlan.zhihu.com/p/533907821)
- [K-Lane: Lidar Lane Dataset and Benchmark for Urban Roads and Highways](https://arxiv.org/abs/2110.11048)
- [LETR: Line Segment Detection Using Transformers without Edges](https://arxiv.org/abs/2101.01909) CVPR 2021 oral
- [HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps](https://openaccess.thecvf.com/content/CVPR2021/papers/Mi_HDMapGen_A_Hierarchical_Graph_Generative_Model_of_High_Definition_Maps_CVPR_2021_paper.pdf) CVPR 2021 [HD mapping]
- [SketchRNN: A Neural Representation of Sketch Drawings](https://arxiv.org/abs/1704.03477) [David Ha]
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://arxiv.org/abs/2002.10880) ICML 2020
- [SOLQ: Segmenting Objects by Learning Queries](https://arxiv.org/abs/2106.02351) NeurlPS 2021 [Megvii, end-to-end, instance segmentation]
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://arxiv.org/abs/2208.03543) 3DV 2022
- [MVSTER: Epipolar Transformer for Efficient Multi-View Stereo](https://arxiv.org/abs/2204.07346) ECCV 2022
- [MOVEDepth: Crafting Monocular Cues and Velocity Guidance for Self-Supervised Multi-Frame Depth Learning](https://arxiv.org/abs/2208.09170) [MVS + monodepth]
- [SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation](https://arxiv.org/abs/2204.03636)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://arxiv.org/abs/2106.08417) [prediction, Waymo] ICLR 2022
- [SSIA: Monocular Depth Estimation with Self-supervised Instance Adaptation](https://arxiv.org/abs/2004.05821) [VGG team, TTR, test time refinement, CVD]
- [CoMoDA: Continuous Monocular Depth Adaptation Using Past Experiences](https://openaccess.thecvf.com/content/WACV2021/papers/Kuznietsov_CoMoDA_Continuous_Monocular_Depth_Adaptation_Using_Past_Experiences_WACV_2021_paper.pdf) WACV 2021
- [MonoRec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera](https://arxiv.org/abs/2011.11814) CVPR 2021 [Daniel Cremmers]
- [Plenoxels: Radiance Fields without Neural Networks](https://arxiv.org/abs/2112.05131)
- [Lidar with Velocity: Motion Distortion Correction of Point Clouds from Oscillating Scanning Lidars](https://arxiv.org/abs/2111.09497) [Livox, ISEE]
- [NWD: A Normalized Gaussian Wasserstein Distance for Tiny Object Detection](https://arxiv.org/abs/2110.13389)
- [Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation](https://arxiv.org/abs/2111.07971) NeurIPS 2021 [Sanja Fidler]
- [Insta-DM: Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency](https://arxiv.org/abs/2102.02629) AAAI 2021
- [Instance-wise Depth and Motion Learning from Monocular Videos](https://arxiv.org/abs/1912.09351) NeurIPS 2020 workshop [[website](https://sites.google.com/site/seokjucv/home/instadm)]
- [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](https://arxiv.org/abs/2003.08934) ECCV 2020 oral
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://arxiv.org/abs/2104.06405) ICCV 2021 oral
- [NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo](https://arxiv.org/abs/2109.01129) ICCV 2021 oral
- [YOLinO: Generic Single Shot Polyline Detection in Real Time](https://arxiv.org/abs/2103.14420) ICCV 2021 workshop [lld]
- [MonoRCNN: Geometry-based Distance Decomposition for Monocular 3D Object Detection](https://arxiv.org/abs/2104.03775) ICCV 2021
- [MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation](https://arxiv.org/abs/2110.00464) ICCV 2021 workshop
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://arxiv.org/abs/1912.13192) CVPR 2020 [Waymo challenge 2nd place]
- [Geometry-based Distance Decomposition for Monocular 3D Object Detection](https://arxiv.org/abs/2104.03775) ICCV 2021 [mono3D]
- [Offboard 3D Object Detection from Point Cloud Sequences](https://arxiv.org/abs/2103.05073) CVPR 2021 [Charles Qi]
- [FreeAnchor: Learning to Match Anchors for Visual Object Detection](https://arxiv.org/abs/1909.02466) NeurIPS 2019
- [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://arxiv.org/abs/2007.03496)
- [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://arxiv.org/abs/2007.08103) ECCV 2020
- [FOVEA: Foveated Image Magnification for Autonomous Navigation](https://arxiv.org/abs/2108.12102) ICCV 2021 [Argo]
- [PifPaf: Composite Fields for Human Pose Estimation](https://arxiv.org/abs/1903.06593) CVPR 2019
- [Monocular 3D Localization of Vehicles in Road Scenes](https://avvision.xyz/iccv21/papers/1/CameraReady/01.pdf) ICCV 2021 workshop [mono3D, tracking]
- [TransformerFusion: Monocular RGB Scene Reconstruction using Transformers](https://arxiv.org/abs/2107.02191)
- [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152)
- [Anchor DETR: Query Design for Transformer-Based Detector](https://arxiv.org/abs/2109.07107) [megvii]
- [PGD: Probabilistic and Geometric Depth: Detecting Objects in Perspective](https://arxiv.org/abs/2107.14160) CoRL 2021
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://arxiv.org/abs/1904.07399)
- [What Makes for End-to-End Object Detection?](https://proceedings.mlr.press/v139/sun21b.html) PMLR 2021
- [Instances as Queries](https://arxiv.org/abs/2105.01928) ICCV 2021 [instance segmentation]
- [One Million Scenes for Autonomous Driving: ONCE Dataset](https://arxiv.org/abs/2106.11037) [Huawei]
- [NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis](https://arxiv.org/abs/2112.12577) 3DV 2021
- [Is 2D Heatmap Representation Even Necessary for Human Pose Estimation?](https://arxiv.org/abs/2107.03332)
- [Topology Preserving Local Road Network Estimation from Single Onboard Camera Image](https://arxiv.org/abs/2112.10155) [BEVNet, Luc Van Gool]
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://arxiv.org/abs/2311.16452) [Small LLM prompting, Microsoft]
- [CoT: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) NeurIPS 2022
- [ToT: Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601) [[Notes](paper_notes/tot.md)] NeurIPS 2023 Oral
- [Cumulative Reasoning with Large Language Models](https://arxiv.org/abs/2308.04371)
- [A Survey of Techniques for Maximizing LLM Performance](https://www.youtube.com/watch?v=ahnGLM-RC1Y&ab_channel=OpenAI) [OpenAI]
- [Drive AGI](https://github.com/OpenDriveLab/DriveAGI)
- [Harnessing the Power of Multi-Modal LLMs for Autonomy](https://www.ghostautonomy.com/blog/mllms-for-autonomy) [Ghost Autonomy]
- [Language to Rewards for Robotic Skill Synthesis](https://arxiv.org/abs/2306.08647)
- [ALOHA: Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://arxiv.org/abs/2304.13705)
- [LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent](https://arxiv.org/abs/2309.12311) [UM]
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://arxiv.org/abs/2207.04429) [Sergey Levine]
- [A Survey of Embodied AI: From Simulators to Research Tasks](https://arxiv.org/abs/2103.04918) IEEE TETCI 2021
- [Habitat Challenge 2021](https://aihabitat.org/challenge/2021/)
- [Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions](https://arxiv.org/abs/2304.04227)
- [DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment](https://arxiv.org/abs/2307.00329) [Jianyu Chen]
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) EMNLP 2021
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://arxiv.org/abs/2201.07207) ICML 2022
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://arxiv.org/abs/2209.11302) ICRA 2023
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation](https://arxiv.org/abs/2209.05451) CoRL 2022
- [LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale](https://arxiv.org/abs/2208.07339) NeurIPS 2022 [LLM Quant]
- [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://arxiv.org/abs/2306.00978) [Song Han, LLM Quant]
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864)
- [CoDi: Any-to-Any Generation via Composable Diffusion](https://arxiv.org/abs/2305.11846) NeurIPS 2023
- [What if a Vacuum Robot has an Arm?](https://ieeexplore.ieee.org/abstract/document/10202493) UR 2023
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://arxiv.org/abs/2205.14135)
- [GPT in 60 Lines of NumPy](https://jaykmody.com/blog/gpt-from-scratch/)
- [Speeding up the GPT - KV cache](https://www.dipkumar.dev/becoming-the-unbeatable/posts/gpt-kvcache/)
- [LLM Parameter Counting](https://kipp.ly/transformer-param-count/)
- [Transformer Inference Arithmetic](https://kipp.ly/transformer-inference-arithmetic/#kv-cache)
- [ALBEF: Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://arxiv.org/abs/2107.07651) NeurIPS 2021 [Junnan Li]
- [CLIP: Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) ICLR 2021 [OpenAI]
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) ICML 2022 [Junnan Li]
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) [Junnan Li]
- [MOO: Open-World Object Manipulation using Pre-trained Vision-Language Models](https://arxiv.org/abs/2303.00905) [Google Robotics, end-to-end visuomotor]
- [VC-1: Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?](https://arxiv.org/abs/2303.18240)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://arxiv.org/abs/2109.12098) CoRL 2021 [Nvidia, end-to-end visuomotor]
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://arxiv.org/abs/2210.17323) ICLR 2023
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://arxiv.org/abs/2211.10438) ICML 2023 [Song Han, LLM Quant]
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://arxiv.org/abs/2003.08515) CVPR 2020
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://arxiv.org/abs/1709.07871) AAAI 2018
- [TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?](https://arxiv.org/abs/2106.11297) NeurIPS 2021
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314)
- [OVO: Open-Vocabulary Occupancy](https://arxiv.org/abs/2305.16133)
- [Code Llama: Open Foundation Models for Code](https://arxiv.org/abs/2308.12950)
- [Chinchilla: Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556) [DeepMind]
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://arxiv.org/abs/2305.13245)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864)
- [RH20T: A Robotic Dataset for Learning Diverse Skills in One-Shot](https://arxiv.org/abs/2307.00595)
- [Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation]()
- [VIMA: General Robot Manipulation with Multimodal Prompts]()
- [An Attention Free Transformer](https://arxiv.org/abs/2105.14103) [Apple]
- [PDDL Planning with Pretrained Large Language Models](https://arxiv.org/abs/2305.11014) [MIT, Leslie Kaelbling]
- [Task and Motion Planning with Large Language Models for Object Rearrangement](https://arxiv.org/abs/2303.06247) IROS 2023