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https://github.com/TianhaoFu/Awesome-3D-Object-Detection

Papers, code and datasets about deep learning for 3D Object Detection.
https://github.com/TianhaoFu/Awesome-3D-Object-Detection
List: Awesome-3D-Object-Detection
3d-representation autonomous-driving computer-vision cvpr cvpr2020 cvpr2021 cvpr2022 deep-learning deep-neural-networks machine-learning object-detection paper papers point-cloud point-cloud-detection point-cloud-processing point-clouds python reading-list study-notes
Last synced: 3 months ago
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Papers, code and datasets about deep learning for 3D Object Detection.
Host: GitHub
URL: https://github.com/TianhaoFu/Awesome-3D-Object-Detection
Owner: TianhaoFu
Created: 2022-03-01T02:04:36.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2023-12-01T03:21:22.000Z (11 months ago)
Last Synced: 2024-05-21T12:18:51.493Z (6 months ago)
Topics: 3d-representation, autonomous-driving, computer-vision, cvpr, cvpr2020, cvpr2021, cvpr2022, deep-learning, deep-neural-networks, machine-learning, object-detection, paper, papers, point-cloud, point-cloud-detection, point-cloud-processing, point-clouds, python, reading-list, study-notes
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Metadata Files:
- Readme: README.md
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# Awesome-3D-Object-Detection

A curated list of research in 3D Object Detection(**Lidar-based Method**). 

You are very welcome to pull request to update this list. :smiley:   

![3D Object Detection](https://github.com/TianhaoFu/Awesome-3D-Object-Detection/blob/main/3d.png)

## Dataset

- [KITTI Dataset](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d)

  - 3,712 training samples

  - 3,769 validation samples

  - 7,518 testing samples

- [nuScenes Dataset](https://www.nuscenes.org/)

  - 28k training samples

  - 6k validation samples

  - 6k testing samples

- [Lyft Dataset](https://level-5.global/data/perception/)

- [Waymo Open Dataset](https://waymo.com/open/download/)

  - 798 training sequences with around 158, 361 LiDAR samples

  - 202 validation sequences with 40, 077 LiDAR samples.

## Top conference & workshop

### Conferene

- Conference on Computer Vision and Pattern Recognition(CVPR)

- International Conference on Computer Vision(ICCV)

- European Conference on Computer Vision(ECCV)

### Workshop

- CVPR 2019 Workshop on Autonomous Driving([nuScenes 3D detection](http://cvpr2019.wad.vision/))

- CVPR 2020 Workshop on Autonomous Driving([BDD1k 3D tracking](http://cvpr2020.wad.vision/))

- CVPR 2021 Workshop on Autonomous Driving([waymo 3D detection](http://cvpr2021.wad.vision/))

- CVPR 2022 Workshop on Autonomous Driving([waymo 3D detection](http://cvpr2022.wad.vision/))

- [CVPR 2021 Workshop on 3D Vision and Robotics](https://sites.google.com/view/cvpr2021-3d-vision-robotics)

- [CVPR 2021 Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics](https://scene-understanding.com/)

- [ICCV 2019 Workshop on Autonomous Driving](http://wad.ai/)

- [ICCV 2021 Workshop on Autonomous Vehicle Vision (AVVision)](https://avvision.xyz/iccv21/), [note](https://openaccess.thecvf.com/content/ICCV2021W/AVVision/papers/Fan_Autonomous_Vehicle_Vision_2021_ICCV_Workshop_Summary_ICCVW_2021_paper.pdf)

- [ICCV 2021 Workshop SSLAD Track 2 - 3D Object Detection](https://competitions.codalab.org/competitions/33236#learn_the_details)

- [ECCV 2020 Workshop on Commands for Autonomous Vehicles](https://c4av-2020.github.io/)

- [ECCV 2020 Workshop on Perception for Autonomous Driving](https://sites.google.com/view/pad2020)

## Paper (Lidar-based method)

- End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds [paper](https://github.com/Tom-Hardy-3D-Vision-Workshop/awesome-3D-object-detection/blob/master)

- Vehicle Detection from 3D Lidar Using Fully Convolutional Network(baidu) [paper](https://arxiv.org/abs/1608.07916)

- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [paper](https://arxiv.org/pdf/1711.06396.pdf)

- Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks [paper](https://arxiv.org/pdf/1805.08689.pdf)

- RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving [paper](https://www.onacademic.com/detail/journal_1000040467923610_4dfe.html)

- BirdNet: a 3D Object Detection Framework from LiDAR information [paper](https://arxiv.org/pdf/1805.01195.pdf)

- LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDAR [paper](https://arxiv.org/pdf/1805.04902.pdf)

- HDNET: Exploit HD Maps for 3D Object Detection [paper](https://link.zhihu.com/?target=http%3A//proceedings.mlr.press/v87/yang18b/yang18b.pdf)

- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [paper](https://arxiv.org/pdf/1612.00593.pdf)

- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space [paper](https://arxiv.org/abs/1706.02413)

- IPOD: Intensive Point-based Object Detector for Point Cloud [paper](https://arxiv.org/abs/1812.05276v1)

- PIXOR: Real-time 3D Object Detection from Point Clouds [paper](http://www.cs.toronto.edu/~wenjie/papers/cvpr18/pixor.pdf)

- DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet [paper](https://www.baidu.com/link?url=EaE2zYjHkWvF33nsET2eNvbFGFu8-D3wWPia04uyKm95jMetHsSv3Zk-tODPGm5clsgCUgtVULsZ6IQqv0EYS_Z8El7Zzh57XzlJroSkaOuC8yv7r1XXL4bUrM2tWrTgjwqzfMV2tMTnFNbMOmHLTkUobgMg7HKoS6WW6PfQzkG&wd=&eqid=8f320cfa0005b878000000055e528b6d)

- Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds [paper](https://arxiv.org/ftp/arxiv/papers/1907/1907.05286.pdf)

- STD: Sparse-to-Dense 3D Object Detector for Point Cloud [paper](https://arxiv.org/abs/1907.10471)

- Fast Point R-CNN [paper](https://arxiv.org/abs/1908.02990)

- StarNet: Targeted Computation for Object Detection in Point Clouds [paper](https://arxiv.org/abs/1908.11069)

- Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection [paper](https://arxiv.org/abs/1908.09492v1)

- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [paper](https://arxiv.org/abs/1903.08701v1)

- FVNet: 3D Front-View Proposal Generation for Real-Time Object Detection from Point Clouds[ paper](https://arxiv.org/abs/1903.10750v1)

- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [paper](https://arxiv.org/abs/1907.03670v1)

- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [paper](https://arxiv.org/abs/1812.04244)

- Complex-YOLO: Real-time 3D Object Detection on Point Clouds [paper](https://arxiv.org/abs/1803.06199)

- YOLO4D: A ST Approach for RT Multi-object Detection and Classification from LiDAR Point Clouds [paper](https://github.com/Tom-Hardy-3D-Vision-Workshop/awesome-3D-object-detection/blob/master)

- YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud [paper](https://arxiv.org/abs/1808.02350)

- Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud [paper](https://arxiv.org/pdf/1903.09847.pdf)

- Pillar-based Object Detection for Autonomous Driving (ECCV2020) [paper](https://arxiv.org/abs/2007.10323)

- EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection(ECCV2020) [paper](https://arxiv.org/abs/2007.08856)

- Multi-Echo LiDAR for 3D Object Detection(ICCV2021) [paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Man_Multi-Echo_LiDAR_for_3D_Object_Detection_ICCV_2021_paper.pdf)

- LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector(ICCV2021) [paper](https://arxiv.org/abs/2108.08258)

- SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation(ICCV2021) [paper](https://arxiv.org/abs/2108.06709)

- Structure Aware Single-stage 3D Object Detection from Point Cloud（CVPR2020) [paper](http://openaccess.thecvf.com/content_CVPR_2020/html/He_Structure_Aware_Single-Stage_3D_Object_Detection_From_Point_Cloud_CVPR_2020_paper.html) [code](https://github.com/skyhehe123/SA-SSD)

- MLCVNet: Multi-Level Context VoteNet for 3D Object Detection（CVPR2020) [paper](https://arxiv.org/abs/2004.05679) [code](https://github.com/NUAAXQ/MLCVNet)

- 3DSSD: Point-based 3D Single Stage Object Detector（CVPR2020） [paper](https://arxiv.org/abs/2002.10187) [code](https://github.com/tomztyang/3DSSD)

- LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention（CVPR2020） [paper](https://arxiv.org/abs/2004.01389) [code](https://github.com/yinjunbo/3DVID)

- PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection(CVPR2020) [paper](https://arxiv.org/abs/1912.13192) [code](https://github.com/sshaoshuai/PV-RCNN)

- Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud（CVPR2020） [paper](https://arxiv.org/abs/2003.01251) [code](https://github.com/WeijingShi/Point-GNN)

- MLCVNet: Multi-Level Context VoteNet for 3D Object Detection（CVPR2020） [paper](https://arxiv.org/pdf/2004.05679)

- Density Based Clustering for 3D Object Detection in Point Clouds（CVPR2020） [paper](http://openaccess.thecvf.com/content_CVPR_2020/papers/Ahmed_Density-Based_Clustering_for_3D_Object_Detection_in_Point_Clouds_CVPR_2020_paper.pdf)

- What You See is What You Get: Exploiting Visibility for 3D Object Detection（CVPR2020) [paper](https://arxiv.org/pdf/1912.04986.pdf)

- PointPainting: Sequential Fusion for 3D Object Detection(CVPR2020) [paper](https://arxiv.org/pdf/1911.10150.pdf)

- HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection（CVPR2020) [paper](https://arxiv.org/pdf/2003.00186)

- LiDAR R-CNN: An Efficient and Universal 3D Object Detector（CVPR2021) [paper](https://arxiv.org/abs/2103.15297)

- Center-based 3D Object Detection and Tracking(CVPR2021) [paper](https://arxiv.org/abs/2006.11275)

- 3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection(CVPR2021) [paper](https://arxiv.org/pdf/2012.04355.pdf)

- Embracing Single Stride 3D Object Detector with Sparse Transformer(CVPR2022) [paper](https://arxiv.org/pdf/2112.06375.pdf), [code](https://github.com/TuSimple/SST)

- Point Density-Aware Voxels for LiDAR 3D Object Detection(CVPR2022) [paper](https://arxiv.org/abs/2203.05662), [code](https://github.com/TRAILab/PDV)

- A Unified Query-based Paradigm for Point Cloud Understanding(CVPR2022) [paper](https://arxiv.org/abs/2203.01252#:~:text=Abstract%3A%203D%20point%20cloud%20understanding,including%20detection%2C%20segmentation%20and%20classification.)

- Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds(CVPR2022) [paper](https://arxiv.org/abs/2203.01252#:~:text=Abstract%3A%203D%20point%20cloud%20understanding,including%20detection%2C%20segmentation%20and%20classification.), [code](https://github.com/Ghostish/Open3DSOT)

- Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds(CVPR2022) [paper](https://arxiv.org/abs/2203.11139), [code](https://github.com/yifanzhang713/IA-SSD)

- Back To Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement(CVPR2022) [paper](http://arxiv.org/abs/2203.05238), [code](https://github.com/xuxw98/BackToReality)

- Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds(CVPR2022) [paper](https://www4.comp.polyu.edu.hk/~cslzhang/paper/VoxSeT_cvpr22.pdf), [code](https://github.com/skyhehe123/VoxSeT)

- BoxeR: Box-Attention for 2D and 3D Transformers(CVPR2022) [paper](https://arxiv.org/abs/2111.13087), [code](https://github.com/kienduynguyen/boxer), [中文介绍](https://mp.weixin.qq.com/s/UnUJJBwcAsRgz6TnQf_b7w)

- Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes(CVPR2022) [paper](https://arxiv.org/abs/2011.12001), [code](https://github.com/qq456cvb/CanonicalVoting)

- DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection(CVPR2022) [paper](https://arxiv.org/abs/2203.08195), [code](https://github.com/tensorflow/lingvo)

- TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers. (CVPR2022) [paper](https://arxiv.org/abs/2203.11496), [code](https://github.com/xuyangbai/transfusion)

- Point2Seq: Detecting 3D Objects as Sequences. (CVPR2022) [paper](https://arxiv.org/abs/2203.13394), [code](https://github.com/ocnflag/point2seq)

- CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection(CVPR2022) [paper](https://arxiv.org/abs/2204.00325)

- LiDAR Snowfall Simulation for Robust 3D Object Detection(CVPR2022) [paper](https://arxiv.org/abs/2203.15118), [code](https://github.com/syscv/lidar_snow_sim)

- Unified Transformer Tracker for Object Tracking(CVPR2022) [paper](https://arxiv.org/abs/2203.15175), [code](https://github.com/visionml/pytracking)

- Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion(CVPR2022) [paper](https://arxiv.org/abs/2203.09780)

- M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation(CVPR2022) [paper](https://arxiv.org/abs/2204.05088)

- RBGNet: Ray-based Grouping for 3D Object Detection(CVPR2022) [paper](https://arxiv.org/abs/2204.02251), [code](https://github.com/haiyang-w/rbgnet)

- Fast Point Transformer(CVPR2022) [paper](https://arxiv.org/abs/2112.04702)

- Focal Sparse Convolutional Networks for 3D Object Detection(CVPR2022) [paper](https://jiaya.me/papers/cvpr22_FocalSparseConv.pdf), [code](https://github.com/dvlab-research/FocalsConv)

- FUTR3D: A Unified Sensor Fusion Framework for 3D Detection(CVPR2022) [paper](https://arxiv.org/abs/2203.10642)

- VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention(CVPR2022) [paper](https://arxiv.org/abs/2203.09704), [code](https://github.com/Gorilla-Lab-SCUT/VISTA)

- OccAM’s Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data(CVPR2022) [paper](https://link.zhihu.com/?target=https%3A//arxiv.org/pdf/2204.06577.pdf)

- Voxel Field Fusion for 3D Object Detection(CVPR2022) [paper](https://arxiv.org/pdf/2205.15938.pdf), [code](https://github.com/dvlab-research/VFF)

- FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels

- LinK: Linear Kernel for LiDAR-based 3D Perception(CVPR2023) [paper](https://arxiv.org/abs/2303.16094), [code](https://github.com/MCG-NJU/LinK)

- DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets(CVPR2023) [paper](https://arxiv.org/abs/2301.06051), [code](https://github.com/Haiyang-W/DSVT)

- VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking(CVPR2023) [paper](https://arxiv.org/abs/2303.11301), [code](https://github.com/dvlab-research/VoxelNeXt)

- LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs(CVPR2023) [paper](https://arxiv.org/abs/2206.10555),  [code](https://github.com/dvlab-research/LargeKernel3D)

- FocalFormer3D : Focusing on Hard Instance for 3D Object Detection(ICCV2023) [paper](https://arxiv.org/abs/2308.04556), [code](https://github.com/NVlabs/FocalFormer3D)

- CTRL: Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection(ICCV2023)  [paper](https://arxiv.org/abs/2304.12315), [code](https://github.com/tusen-ai/SST)

- Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection(arxiv2023) [paper](https://arxiv.org/abs/2305.12853), [code](https://github.com/JinglinZhan/Real-Aug)

## Competition Solution

- [6th AI Driving Olympics, ICRA 2021](https://driving-olympics.ai/)

- [5th AI Driving Olympics, NeurIPS 2020](https://driving-olympics.ai/)

- [Workshop on Benchmarking Progress in Autonomous Driving, ICRA 2020](http://montrealrobotics.ca/driving-benchmarks/)

- [Workshop on Autonomous Driving, CVPR 2019](https://sites.google.com/view/wad2019)

## Engineering

- Pointpillars-ONNX [code](https://github.com/SmallMunich/nutonomy_pointpillars)

- Centerpoint-ONNX [code](https://github.com/CarkusL/CenterPoint)

- BEVFormer-TensorRT [code](https://github.com/DerryHub/BEVFormer_tensorrt)

## Survey

- 2021.04 Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy [paper](https://www.sciencedirect.com/science/article/abs/pii/S1566253520304097)

- 2021.07 3D Object Detection for Autonomous Driving: A Survey [paper](https://arxiv.org/abs/2106.10823)

- 2021.07 Multi-Modal 3D Object Detection in Autonomous Driving: a Survey [paper](https://arxiv.org/abs/2106.12735)

- 2021.10 A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving [paper](https://www.sciencedirect.com/science/article/abs/pii/S0097849321001321)

- 2021.12 Deep Learning for 3D Point Clouds: A Survey [paper](https://ieeexplore.ieee.org/abstract/document/9127813)

## Book

- 3D Object Detection Algorithms Based on Lidar and Camera: Design and Simulation [book](https://www.amazon.com/Object-Detection-Algorithms-Based-Camera/dp/6200536538)

## Video

- Aivia online workshop: 3D object detection and tracking [video](https://www.youtube.com/watch?v=P0TrkwAdFYQ)

- 3D Object Retrieval 2021 workshop [video](https://3dor2021.github.io/programme.html)

- 3D Deep Learning Tutorial from SU lab at UCSD [video](https://www.youtube.com/watch?v=vfL6uJYFrp4)

- Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) [video](https://www.youtube.com/watch?v=vfL6uJYFrp4)

- Current Approaches and Future Directions for Point Cloud Object (2021.04) [video](https://www.youtube.com/watch?v=xFFCQVwYeec)

- Latest 3D OBJECT DETECTION with 30+ FPS on CPU - MediaPipe and OpenCV Python (2021.05) [video](https://www.youtube.com/watch?v=f-Ibri14KMY)

- MIT autonomous driving seminar (2019.11) [video](https://space.bilibili.com/174493426/channel/series)

- sensetime seminar1 [video](https://www.bilibili.com/video/BV1Bf4y1b7PF?spm_id_from=333.999.0.0)

- sensetime seminar2 [slides](https://docs.google.com/presentation/d/11CoKCxRFgzbIujMXxTZjHDo_hV0arEQ7sUFWFXWaX8o/edit#slide=id.p1)

## Course

- [University of Toronto, csc2541](http://www.cs.toronto.edu/~urtasun/courses/CSC2541/06_3D_detection.pdf)

- [University of Tübingen, Self-Driving Cars](https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/) *(Strong Recommendation)*

- [baidu-Udacity](https://apollo.auto/devcenter/devcenter.html)

- [baidu-apollo](http://bit.baidu.com/Subject/index/id/16.html)

- [University of Toronto, coursera](https://www.coursera.org/specializations/self-driving-cars?ranMID=40328&ranEAID=9IqCvd3EEQc&ranSiteID=9IqCvd3EEQc-MlZGCwEU2294XsVYWDNwzw&siteID=9IqCvd3EEQc-MlZGCwEU2294XsVYWDNwzw&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=9IqCvd3EEQc)

## Blog

- [Waymo Blog](https://blog.waymo.com/)

- [apollo介绍之Perception模块](https://zhuanlan.zhihu.com/p/142401769)

- [Apollo notes (Apollo学习笔记) - Apollo learning notes for beginners.](https://github.com/daohu527/Dig-into-Apollo#ledger-%E7%9B%AE%E5%BD%95)

- [PointNet系列论文解读](https://zhuanlan.zhihu.com/p/44809266)

- [Deep3dBox: 3D Bounding Box Estimation Using Deep Learning and Geometry](https://patrick-llgc.github.io/Learning-Deep-Learning/paper_notes/deep3dbox.html)

- [SECOND算法解析](https://zhuanlan.zhihu.com/p/356892010)

- [PointRCNN深度解读](https://zhuanlan.zhihu.com/p/361973979)

- [Fast PointRCNN论文解读](https://zhuanlan.zhihu.com/p/363926237)

- [PointPillars论文和代码解析](https://zhuanlan.zhihu.com/p/357626425)

- [VoxelNet论文和代码解析](https://zhuanlan.zhihu.com/p/352419316)

- [CenterPoint源码分析](https://zhuanlan.zhihu.com/p/444447881)

- [PV-RCNN: 3D目标检测 Waymo挑战赛+KITTI榜 单模态第一算法](https://zhuanlan.zhihu.com/p/148942116)

- [LiDAR R-CNN：一种快速、通用的二阶段3D检测器](https://zhuanlan.zhihu.com/p/359800738)

- [混合体素网络（HVNet)](https://zhuanlan.zhihu.com/p/122426949)

- [自动驾驶感知| Range Image paper分享](https://zhuanlan.zhihu.com/p/420708905)

- [SST：单步长稀疏Transformer 3D物体检测器](https://zhuanlan.zhihu.com/p/476056546)

## Famous Research Group/Scholar

- [Naiyan Wang@Tusimple](https://scholar.google.com/citations?user=yAWtq6QAAAAJ&hl=en)

- [Hongsheng Li@CUHK](https://scholar.google.com/citations?user=BN2Ze-QAAAAJ&hl=en)

- [Oncel Tuzel@Apple](https://scholar.google.com/citations?user=Fe7NTe0AAAAJ&hl=en)

- [Oscar Beijbom@nuTonomy](https://scholar.google.com/citations?user=XP_Hxm4AAAAJ&hl=en)

- [Raquel Urtasun@University of Toronto](https://scholar.google.com/citations?user=jyxO2akAAAAJ&hl=en)

- [Philipp Krähenbühl@UT Austin](https://scholar.google.com/citations?hl=en&user=dzOd2hgAAAAJ&view_op=list_works&sortby=pubdate)

- [Deva Ramanan@CMU](https://scholar.google.com/citations?hl=en&user=9B8PoXUAAAAJ&view_op=list_works&sortby=pubdate)

- [Jiaya Jia@CUHK](https://jiaya.me/)

- [Thomas Funkhouser@princeton](https://www.cs.princeton.edu/~funk/)

- [Leonidas Guibas@Stanford](https://scholar.google.com/citations?hl=en&user=5JlEyTAAAAAJ&view_op=list_works&sortby=pubdate)

- [Steven Waslander@University of Toronto](https://www.trailab.utias.utoronto.ca/)

- [Ouais Alsharif@Google Brain](https://scholar.google.com/citations?hl=en&user=nFefEI8AAAAJ&view_op=list_works&sortby=pubdate)

- [Yuning CHAI(former)@waymo](https://scholar.google.com/citations?hl=en&user=i7U4YogAAAAJ&view_op=list_works&sortby=pubdate)

- [Yulan Guo@NUDT](http://yulanguo.me/)

- [Lei Zhang@The Hong Kong Polytechnic University](https://www4.comp.polyu.edu.hk/~cslzhang/)

- [Hongyang Li@sensetime](https://lihongyang.info/)

- [Luc Van Gool@ETH](https://scholar.google.com/citations?user=TwMib_QAAAAJ&hl=en)

- [Sanja Fidler@NVIDIA](https://scholar.google.com/citations?hl=en&user=CUlqK5EAAAAJ&view_op=list_works&citft=1&email_for_op=tianhaofu1%40gmail.com&sortby=pubdate)

- [Alan L. Yuille@JHU](https://www.cs.jhu.edu/~ayuille/)

- [OpenDriveLab](https://github.com/OpenDriveLab)

## Famous CodeBase

- [Point Cloud Library (PCL)](https://github.com/PointCloudLibrary/pcl)

- [Torchsparse](https://github.com/mit-han-lab/torchsparse)

- [Spconv](https://github.com/traveller59/spconv)

- [Det3D](https://github.com/poodarchu/Det3D)

- [mmdetection3d](https://github.com/open-mmlab/mmdetection3d)

- [OpenPCDet](https://github.com/open-mmlab/OpenPCDet)

- [Centerpoint](https://github.com/tianweiy/CenterPoint)

- [Apollo Auto - Baidu open autonomous driving platform](https://github.com/ApolloAuto)

- [AutoWare - The University of Tokyo autonomous driving platform](https://www.autoware.org/)

- [Openpilot - A open source software built to improve upon the existing driver assistance in most new cars on the road today](https://comma.ai/)

- [DeepVision3D](https://github.com/dvlab-research/DeepVision3D)

- [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine)

## Famous Toolkit

- [ZED Box](https://www.stereolabs.com/docs/object-detection/)

# Acknowlegement

[Awesome System for Machine Learning](https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning)

[awesome-3D-object-detection](https://github.com/Tom-Hardy-3D-Vision-Workshop/awesome-3D-object-detection)