https://github.com/zhulf0804/3D-PointCloud

Papers and Datasets about Point Cloud.
https://github.com/zhulf0804/3D-PointCloud

autonomous-driving classification completion datasets detection generation monocular papers point-cloud registration segmentation

Last synced: about 1 year ago
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Papers and Datasets about Point Cloud.

• Host: GitHub
• URL: https://github.com/zhulf0804/3D-PointCloud
• Owner: zhulf0804
• Created: 2019-03-06T05:21:06.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-08-25T10:26:56.000Z (over 1 year ago)
• Last Synced: 2024-08-25T11:36:14.107Z (over 1 year ago)
• Topics: autonomous-driving, classification, completion, datasets, detection, generation, monocular, papers, point-cloud, registration, segmentation
• Language: Python
• Homepage:
• Size: 1.69 MB
• Stars: 2,329
• Watchers: 89
• Forks: 295
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-artificial-intelligence-research - 3D Point Cloud - point cloud deep learning resources. (Multimodal, Vision-Language, and Generative AI / Computer Vision)
• awesome-segmentation-all-types - 3D-PointCloud
• awesome-image-registration - 3D-PointCloud

README

          
# 3D - Point Cloud

**Paper list** and **Datasets** about Point Cloud. Datasets can be found in [Datasets.md](https://github.com/zhulf0804/3D-PointCloud/blob/master/Datasets.md).



## Survey papers

- [A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning](https://arxiv.org/pdf/2404.13830v1.pdf) [IJCAI 2024; [Github](https://github.com/yxzhang15/PCR)]

- [Sequential Point Clouds: A Survey](https://arxiv.org/pdf/2204.09337.pdf) [TPAMI 2024]

- [A Survey of Label-Efficient Deep Learning for 3D Point Clouds](https://arxiv.org/pdf/2305.19812.pdf) [TPAMI 2024; [Github](https://github.com/xiaoaoran/3D_label_efficient_learning)]

- [Surface Reconstruction from Point Clouds: A Survey and a Benchmark](https://arxiv.org/pdf/2205.02413.pdf) [TPAMI 2024]

- [End-to-end Autonomous Driving: Challenges and Frontiers](https://arxiv.org/pdf/2306.16927.pdf) [TPAMI 2024; [Github](https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving)]

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://arxiv.org/pdf/2206.09474.pdf) [IJCV 2023; [Github](https://github.com/pointscoder/awesome-3d-object-detection-for-autonomous-driving)]

- [Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey](https://arxiv.org/pdf/2202.13589.pdf) [TPAMI 2023; [Github](https://github.com/xiaoaoran/3d_url_survey)]

- [3D Object Detection from Images for Autonomous Driving: A Survey](https://arxiv.org/pdf/2202.02980.pdf) [TPAMI 2023; [Github](https://github.com/xinzhuma/3dodi-survey)]

- [Survey and Systematization of 3D Object Detection Models and Methods](https://arxiv.org/pdf/2201.09354v1.pdf) [TVC 2023]

- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://arxiv.org/pdf/2106.12735.pdf) [IJCV 2023]

- [Cross-source Point Cloud Registration: Challenges, Progress and Prospects](https://arxiv.org/pdf/2305.13570.pdf) [Neurocomputing 2023]

- [Self-Supervised Learning for Point Clouds Data: A Survey](https://arxiv.org/pdf/2305.11881.pdf) [ESWA 2023]

- [Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey](https://arxiv.org/pdf/2305.04691.pdf) [arXiv 2023]

- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://arxiv.org/pdf/2304.10410v2.pdf) [IEEE T-IV 2023; [Project](https://radar-camera-fusion.github.io)]

- [Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey](https://arxiv.org/pdf/2302.02790.pdf) [IEEE T-IV 2023]

- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://arxiv.org/pdf/2209.05324.pdf) [TPAMI 2023; [Github](https://github.com/OpenDriveLab/BEVPerception-Survey-Recipe)]

- [3D Vision with Transformers: A Survey](https://arxiv.org/pdf/2208.04309.pdf) [arXiv 2022; [Github](https://github.com/lahoud/3d-vision-transformers)]

- [Vision-Centric BEV Perception: A Survey](https://arxiv.org/pdf/2208.02797.pdf) [arXiv 2022; [Github](https://github.com/4DVLab/Vision-Centric-BEV-Perception)]

- [Transformers in 3D Point Clouds: A Survey](https://arxiv.org/pdf/2205.07417.pdf) [arXiv 2022]

- [A Survey of Robust LiDAR-based 3D Object Detection Methods for Autonomous Driving](https://arxiv.org/pdf/2204.00106.pdf) [arXiv 2022]

- [A Survey of Non-Rigid 3D Registration](https://arxiv.org/pdf/2203.07858.pdf) [Eurographics 2022]

- [Comprehensive Review of Deep Learning-Based 3D Point Clouds Completion Processing and Analysis](https://arxiv.org/pdf/2203.03311.pdf) [TITS 2022]

- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://arxiv.org/pdf/2202.02703.pdf) [arXiv 2022]

- [3D Object Detection for Autonomous Driving: A Survey](https://arxiv.org/pdf/2106.10823.pdf) [Pattern Recognition 2022; [Github](https://github.com/rui-qian/SoTA-3D-Object-Detection)]

- [3D Semantic Scene Completion: a Survey](https://arxiv.org/pdf/2103.07466.pdf) [IJCV 2022]

- [Deep Learning based 3D Segmentation: A Survey](https://arxiv.org/pdf/2103.05423.pdf) [arXiv 2021]

- [A comprehensive survey on point cloud registration](https://arxiv.org/pdf/2103.02690.pdf) [arXiv 2021]

- [Deep Learning for 3D Point Clouds: A Survey](https://arxiv.org/pdf/1912.12033.pdf) [TPAMI 2020; [Github](https://github.com/QingyongHu/SoTA-Point-Cloud)]

- [A Comprehensive Performance Evaluation of 3D Local Feature Descriptors](https://www.researchgate.net/profile/Yulan_Guo/publication/274387466_A_Comprehensive_Performance_Evaluation_of_3D_Local_Feature_Descriptors/links/552b4a5b0cf29b22c9c1a6d2/A-Comprehensive-Performance-Evaluation-of-3D-Local-Feature-Descriptors.pdf) [IJCV 2016]

## 2024

- ECCV

  - [OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation](https://arxiv.org/abs/2309.00616)[`open-vocabulary`; [Github](https://github.com/Pointcept/OpenIns3D)]

  - [Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene](https://arxiv.org/pdf/2407.08569v1) [`det`]

  - [Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection](https://arxiv.org/pdf/2407.08931v1) [`det`]

  - [OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection](https://arxiv.org/pdf/2407.10753v1) [`det`]

  - [SEED: A Simple and Effective 3D DETR in Point Clouds](https://arxiv.org/pdf/2407.10749v1) [`det`]

  - [DSPDet3D: Dynamic Spatial Pruning for 3D Small Object Detection](https://arxiv.org/pdf/2305.03716.pdf) [`det`; [PyTorch](https://github.com/xuxw98/DSPDet3D)]

  - [General Geometry-aware Weakly Supervised 3D Object Detection](https://arxiv.org/pdf/2407.13748v1) [`det`; [PyTorch](https://github.com/gwenzhang/GGA)]

  - [SegPoint: Segment Any Point Cloud via Large Language Model](https://arxiv.org/pdf/2407.13761v1) [`seg`]

  - [Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models](https://arxiv.org/pdf/2407.13642v1) [`seg`]

  - [ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation](https://arxiv.org/pdf/2407.07171v1) [`seg`]

  - [RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation](https://arxiv.org/pdf/2407.10159v1) [`seg`]

  - [3×2: 3D Object Part Segmentation by 2D Semantic Correspondences](https://arxiv.org/pdf/2407.09648v1) [`seg`]

  - [Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation](https://arxiv.org/pdf/2407.12489v1) [`seg`]

  - [SFPNet: Sparse Focal Point Network for Semantic Segmentation on General LiDAR Point Clouds](https://arxiv.org/pdf/2407.11569v1) [`seg`]

  - [HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentation](https://arxiv.org/pdf/2407.12387v1) [`seg`; [PyTorch](https://github.com/tpzou/HGL)]

  - [Part2Object: Hierarchical Unsupervised 3D Instance Segmentation](https://arxiv.org/pdf/2407.10084v1) [`seg`; [PyTorch](https://arxiv.org/pdf/2407.10084v1)]

  - [4D Contrastive Superflows are Dense 3D Representation Learners](https://arxiv.org/pdf/2407.06190v1) [`pre-training`]

  - [Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape Data](https://arxiv.org/pdf/2407.10200v1) [`pre-training`]

  - [Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completion](https://arxiv.org/pdf/2407.02887) [`completion`; [PyTorch](https://github.com/WHU-USI3DV/EGIInet)]

  - [T-CorresNet: Template Guided 3D Point Cloud Completion with Correspondence Pooling Query Generation Strategy](https://arxiv.org/pdf/2407.05008) [`completion`; [Github](https://github.com/df-boy/T-CorresNet)]

  - [GaussReg: Fast 3D Registration with Gaussian Splatting](https://arxiv.org/pdf/2407.05254v1) [`registration`]

  - [PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training](https://arxiv.org/pdf/2407.14054v1) [`registration`; [PyTorch](https://github.com/Chen-Suyi/PointRegGPT)]

  - [PARE-Net: Position-Aware Rotation-Equivariant Networks for Robust Point Cloud Registration](https://arxiv.org/pdf/2407.10142) [`registration`; [PyTorch](https://github.com/yaorz97/PARENet)] 

  - [ML-SemReg: Boosting Point Cloud Registration with Multi-level Semantic Consistency](https://arxiv.org/pdf/2407.09862v1) [`registration`; [PyTorch](https://github.com/Laka-3DV/ML-SemReg)]

  - [Correspondence-Free SE(3) Point Cloud Registration in RKHS via Unsupervised Equivariant Learning](https://arxiv.org/pdf/2407.20223v1) [`registration`; [Project](https://sites.google.com/view/eccv24-equivalign)]

  - [UMERegRobust - Universal Manifold Embedding Compatible Features for Robust Point Cloud Registration](https://arxiv.org/pdf/2408.12380) [`registration`; [PyTorch](https://github.com/yuvalH9/UMERegRobust)]

  - [Transferable 3D Adversarial Shape Completion using Diffusion Models](https://arxiv.org/pdf/2407.10077v1) [`adversarial attack`]

  - [R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection](https://arxiv.org/pdf/2407.10862v1) [`anomaly detection`]

- CVPR

  - [Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis](https://arxiv.org/pdf/2403.01439.pdf) [`pre-training`; [PyTorch](https://github.com/LMD0311/DAPT)]

  - [HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes](https://arxiv.org/pdf/2403.02769v1.pdf) [`det`]

  - [Commonsense Prototype for Outdoor Unsupervised 3D Object Detection](https://arxiv.org/pdf/2404.16493) [`det`; [PyTorch](https://github.com/hailanyi/CPD)]

  - [Learning Occupancy for Monocular 3D Object Detection](https://arxiv.org/pdf/2305.15694.pdf) [`det`; [Github](https://github.com/SPengLiang/OccupancyM3D)]

  - [Point Transformer V3: Simpler, Faster, Stronger](https://arxiv.org/pdf/2312.10035v1.pdf) [`seg`, `det`; [Github](https://github.com/pointcept/pointtransformerv3)]

  - [OneFormer3D: One Transformer for Unified Point Cloud Segmentation](https://arxiv.org/pdf/2311.14405.pdf) [`seg`; [Github](https://github.com/filaPro/oneformer3d)]

  - [Rethinking Few-shot 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2403.00592.pdf) [`seg`; [PyTorch](https://github.com/ZhaochongAn/COSeg)]

  - [CurveCloudNet: Processing Point Clouds with 1D Structure](https://arxiv.org/pdf/2303.12050v1.pdf) [`seg`]

  - [UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes](https://arxiv.org/pdf/2303.14541.pdf) [`seg`; [Project](https://rozdavid.github.io/unscene3d)]

  - [No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation](https://arxiv.org/pdf/2404.04050.pdf) [`seg`; [PyTorch](https://github.com/yangyangyang127/Seg-NN)]

  - [TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentation](https://arxiv.org/pdf/2407.09751v1) [`seg`]

  - [GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds](https://arxiv.org/pdf/2403.19220) [`seg`; [PyTorch](https://github.com/zhangshengjun2019/GeoAuxNet)]

  - [Multi-Space Alignments Towards Universal LiDAR Segmentation](https://arxiv.org/pdf/2405.01538v1) [`seg`; [Github](https://github.com/youquanl/M3Net)]

  - [KPConvX: Modernizing Kernel Point Convolution with Kernel Attention](https://arxiv.org/pdf/2405.13194) [`cls`, `seg`]

  - [X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition](https://arxiv.org/pdf/2404.15010.pdf) [`cls`, `seg`; [PyTorch](https://github.com/sunshuofeng/X-3D)]

  - [Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding](https://arxiv.org/pdf/2312.02244) [`cls`, `seg`; [PyTorch](https://github.com/gfmei/GeoZe)]

  - [Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching](https://arxiv.org/pdf/2402.17372v1.pdf) [`matching`]

  - [Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension](https://arxiv.org/pdf/2403.03532.pdf) [`registration`; [Github](https://github.com/liuQuan98/EYOC)]

  - [Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes](https://arxiv.org/pdf/2404.04557v1.pdf) [`registration`; [Github](https://github.com/zhiyuanYU134/MIRETR)]

  - [FastMAC: Stochastic Spectral Sampling of Correspondence Graph](https://arxiv.org/pdf/2403.08770.pdf) [`registration`; [Github](https://github.com/Forrest-110/FastMAC)]

  - [Scalable 3D Registration via Truncated Entry-wise Absolute Residuals](https://arxiv.org/pdf/2404.00915.pdf) [`registration`; [Github](https://github.com/tyhuang98/TEAR-release)]

  - [Category-Level Multi-Part Multi-Joint 3D Shape Assembly](https://arxiv.org/pdf/2303.06163v1.pdf) [`shape assembly`]

  - [Symphonize 3D Semantic Scene Completion with Contextual Instance Queries](https://arxiv.org/pdf/2306.15670.pdf) [`semantic scene completion`; [PyTorch](https://github.com/hustvl/Symphonies)]

  - [PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation](https://arxiv.org/pdf/2306.10013.pdf) [`semantic occupancy prediction`; [Github](https://github.com/Robertwyq/PanoOcc)]

  - [Visual Point Cloud Forecasting enables Scalable Autonomous Driving](https://arxiv.org/pdf/2312.17655.pdf) [`autonomous driving`; [Github](https://github.com/OpenDriveLab/ViDAR)]

  - [Object Dynamics Modeling with Hierarchical Point Cloud-based Representations](https://arxiv.org/pdf/2312.00068) [`autonomous driving`; [PyTorch](https://github.com/prashkmr/GLiDR)]

  - [Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes](https://arxiv.org/pdf/2403.01414v1.pdf) [`reconstruction`]

  - [Unleashing Network Potentials for Semantic Scene Completion](https://arxiv.org/pdf/2403.07560v1.pdf) [`completion`]

  - [FSC: Few-point Shape Completion](https://arxiv.org/pdf/2403.07359v1.pdf) [`completion`]

  - [Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange](https://arxiv.org/pdf/2404.07504v1.pdf) [`self-supervised`; [Github](https://github.com/YanhaoWu/OESSL)]

  - [GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding](https://arxiv.org/pdf/2403.09639.pdf) [`self-supervised`]

  - [SemCity: Semantic Scene Generation with Triplane Diffusion](https://arxiv.org/pdf/2403.07773v1.pdf) [`generation`]

  - [Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds](https://arxiv.org/pdf/2403.05247.pdf) [`adversarial attack`; [Github](https://github.com/TRLou/HiT-ADV)]

  - [StraightPCF: Straight Point Cloud Filtering](https://arxiv.org/pdf/2405.08322v1) [`filtering`; [Github](https://github.com/ddsediri/StraightPCF)]

  - [Unsupervised Occupancy Learning from Sparse Point Cloud](https://arxiv.org/pdf/2404.02759) [`reconstruction`]

  - [Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysis](https://arxiv.org/pdf/2403.11113) [`rotation invariance`; [PyTorch](https://github.com/wdttt/LocoTrans)]

  - [Text2Loc: 3D Point Cloud Localization from Natural Language](https://arxiv.org/pdf/2311.15977) [`localization`; [PyTorch](https://github.com/Yan-Xia/Text2Loc)]

- AAAI

  - [iDet3D: Towards Efficient Interactive Object Detection for LiDAR Point Clouds](https://arxiv.org/pdf/2312.15449.pdf) [`det`]

  - [CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection](https://arxiv.org/pdf/2403.03721v1.pdf) [`det`]

  - [SimDistill: Simulated Multi-modal Distillation for BEV 3D Object Detection](https://arxiv.org/pdf/2303.16818.pdf) [`det`; [Github](https://github.com/ViTAE-Transformer/BEVSimDet)]

  - [Semi-supervised 3D Object Detection with PatchTeacher and PillarMix](https://arxiv.org/pdf/2407.09787v1) [`det`]

  - [PointCVaR: Risk-optimized Outlier Removal for Robust 3D Point Cloud Classification](https://arxiv.org/pdf/2307.10875.pdf) [`cls`; [PyTorch](https://github.com/shinke-li/pointcvar)]

  - [NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields](https://arxiv.org/pdf/2304.14811.pdf) [`autonomous driving`; [Github](https://github.com/fudan-zvg/NeRF-LiDAR)]

  - [CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal Distance and Multi-scale Geometry](https://arxiv.org/pdf/2312.09154v1.pdf) [`normal estimation`]

  - [3D Visibility-aware Generalizable Neural Radiance Fields for Interacting Hands](https://arxiv.org/pdf/2401.00979v1.pdf) [`NeRF`]

  - [CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers](https://arxiv.org/pdf/2401.01552.pdf) [`completion`; [PyTorch](https://github.com/EasyRy/CRA-PCN)]

  - [PointAttN: You Only Need Attention for Point Cloud Completion](https://arxiv.org/pdf/2203.08485v1.pdf) [`completion`; [PyTorch](https://github.com/ohhhyeahhh/PointAttN)]

  - [EPCL: Frozen CLIP Transformer is An Efficient Point Cloud Encoder](https://arxiv.org/pdf/2212.04098.pdf) [`pre-training`]

  - [MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding](https://arxiv.org/pdf/2402.10002) [`self-supervised`]

  - [DHGCN: Dynamic Hop Graph Convolution Network for Self-Supervised Point Cloud Learning](https://arxiv.org/pdf/2401.02610) [`self-supervised`]

  - [SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration](https://arxiv.org/pdf/2312.08664.pdf) [`registration`]

  - [DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors](https://arxiv.org/pdf/2312.02684.pdf) [`matching`; [Github](https://github.com/ZhangXiaze/DeepPointMap)]

  - [Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning](https://arxiv.org/pdf/2403.05117.pdf) [`upsampling`]

  - [Modeling Continuous Motion for 3D Point Cloud Object Tracking](https://arxiv.org/pdf/2303.07605v1.pdf) [`tracking`]

- Others

  - [3D Geometric Shape Assembly via Efficient Point Cloud Matching](https://arxiv.org/pdf/2407.10542v1) [`assembly`; ICML]

  - [Fully Sparse Fusion for 3D Object Detection](https://arxiv.org/pdf/2304.12310v1.pdf) [`det`; [Github](https://github.com/BraveGroup/FullySparseFusion); TPAMI]

  - [Fast-BEV: A Fast and Strong Bird’s-Eye View Perception Baseline](https://arxiv.org/pdf/2301.12511v1.pdf) [`det`; [PyTorch](https://github.com/Sense-GVT/Fast-BEV)]

  - [An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds](https://arxiv.org/pdf/2303.12535v1.pdf) [`tracking`; [PyTorch](https://github.com/Ghostish/Open3DSOT); TPAMI]

  - [Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer](https://arxiv.org/pdf/2208.05216.pdf) [`tracking`; [PyTorch](https://github.com/Jasonkks/PTTR)]

  - [Efficient and Robust Point Cloud Registration via Heuristics-guided Parameter Search](https://arxiv.org/pdf/2404.06155v1.pdf) [`registration`; [Github](https://github.com/tyhuang98/HERE-release); TPAMI]

  - [RIGA: Rotation-Invariant and Globally-Aware Descriptors for Point Cloud Registration](https://arxiv.org/pdf/2209.13252.pdf) [`registration`; TPAMI]

  - [Benchmarking the Robustness of LiDAR Semantic Segmentation Models](https://arxiv.org/pdf/2301.00970v1.pdf) [`seg`; IJCV]

  - [Position-Guided Point Cloud Panoptic Segmentation Transformer](https://arxiv.org/pdf/2303.13509v1.pdf) [`seg`; [Github](https://github.com/SmartBot-PJLab/P3Former); IJCV]

  - [PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis](https://arxiv.org/pdf/2302.05201.pdf) [`cls`, `seg`; TNNLS]

  - [CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning](https://arxiv.org/pdf/2201.08215v1.pdf) [`self-supervised`; TMM]

  - [Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrast](https://arxiv.org/pdf/2305.19623.pdf) [`self-supervised learning`; [Github](https://github.com/Asterisci/Point-GCC); ACM MM]

  - [StereoScene: BEV-Assisted Stereo Matching Empowers 3D Semantic Scene Completion](https://arxiv.org/pdf/2303.13959v1.pdf) [`semantic scene completion`; [PyTorch](https://github.com/Arlo0o/StereoScene); IJCAI]

  - [Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature](https://arxiv.org/pdf/2309.16023.pdf) [`registration`; [Github](https://github.com/jinsz/Q-REG); 3DV]

  - [Pix4Point: Image Pretrained Transformers for 3D Point Cloud Understanding](https://arxiv.org/pdf/2208.12259.pdf) [`pretraining`; [Github](https://github.com/guochengqian/Pix4Point); 3DV]

  - [OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection](https://arxiv.org/pdf/2306.01738.pdf) [`det`; 3DV]

  - [MAELi -- Masked Autoencoder for Large-Scale LiDAR Point Clouds](https://arxiv.org/pdf/2212.07207.pdf) [`self-supervised`; WACV]

  - [Top-Down Beats Bottom-Up in 3D Instance Segmentation](https://arxiv.org/pdf/2302.02871.pdf) [`seg`; [PyTorch](https://github.com/SamsungLabs/td3d); WACV]

  - [Hierarchical Point Attention for Indoor 3D Object Detection](https://arxiv.org/pdf/2301.02650v2) [`det`; [Github](https://github.com/salesforce/Hierarchical_Point_Attention); ICRA]

  - [LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception](https://arxiv.org/pdf/2303.12194v1.pdf) [`det`, `seg`; ICRA]

  - [FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization](https://arxiv.org/pdf/2309.08966.pdf) [`registration`; ICRA]

  - [SGFeat: Salient Geometric Feature for Point Cloud Registration](https://arxiv.org/pdf/2309.06207.pdf) [`registration`; ICRA]

  - [3D-OAE: Occlusion Auto-Encoders for Self-Supervised Learning on Point Clouds](https://arxiv.org/pdf/2203.14084.pdf) [`self-supervised`; [Github](https://github.com/junshengzhou/3D-OAE); ICRA]

  - [V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems](https://arxiv.org/pdf/2407.10195v1) [`calibration`; IROS]

  - [Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction](https://arxiv.org/pdf/2305.18829.pdf) [`det`, `semantic occupancy prediction`; [PyTorch](https://github.com/chaytonmin/Occ-BEV); RAL]

  - [Robust Partial-to-Partial Point Cloud Registration in a Full Range](https://arxiv.org/pdf/2111.15606.pdf) [`registration`; [PyTorch](https://github.com/paul007pl/GMCNet); RAL]

  - [Joint Representation Learning for Text and 3D Point Cloud](https://arxiv.org/pdf/2301.07584v1.pdf) [`pre-training`; [Github](https://github.com/LeapLabTHU/Text4Point); PR]

  - [POS-BERT: Point Cloud One-Stage BERT Pre-Training](https://arxiv.org/pdf/2204.00989.pdf) [`pre-training`; [Github](https://github.com/fukexue/POS-BERT); ESWA]

- arXiv

  - [Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration](https://arxiv.org/pdf/2401.00436.pdf) [`registration`]

  - [BiEquiFormer: Bi-Equivariant Representations for Global Point Cloud Registration](https://arxiv.org/pdf/2407.08729v1) [`registration`]

## 2023

- ICCV

  - [Scene as Occupancy](https://arxiv.org/pdf/2306.02851.pdf) [`autonomous driving`; [PyTorch](https://github.com/OpenDriveLab/OccNet)]

  - [OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction](https://arxiv.org/pdf/2304.05316v1.pdf) [`semantic occupancy prediction`; [PyTorch](https://github.com/zhangyp15/OccFormer)]

  - [Cross Modal Transformer via Coordinates Encoding for 3D Object Dectection](https://arxiv.org/pdf/2301.01283v1.pdf) [`det`; [Github](https://github.com/junjie18/CMT)]

  - [Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection](https://arxiv.org/pdf/2304.12315v1.pdf) [`det`; [PyTorch](https://github.com/tusen-ai/SST)]

  - [Efficient Transformer-based 3D Object Detection with Dynamic Token Halting](https://arxiv.org/pdf/2303.05078v1.pdf) [`det`]

  - [Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection](https://arxiv.org/pdf/2303.11926v1.pdf) [`det`; [Github](https://github.com/exiawsh/StreamPETR)]

  - [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://arxiv.org/pdf/2206.01256.pdf) [`autonomous driving`; [Github](https://github.com/megvii-research/PETR)]

  - [SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving](https://arxiv.org/abs/2303.09551v1) [`autonomous driving`; [PyTorch](https://github.com/weiyithu/SurroundOcc)]

  - [SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection](https://arxiv.org/pdf/2309.07084v1.pdf) [`det`; [Github](https://github.com/IranQin/SupFusion)]

  - [Object as Query: Lifting any 2D Object Detector to 3D Detection](https://arxiv.org/pdf/2301.02364) [`det`; [PyTorch](https://github.com/tusen-ai/MV2D)]

  - [SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection](https://arxiv.org/pdf/2304.14340v1.pdf) [`det`; [Github](https://github.com/yichen928/SparseFusion)]

  - [DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection](https://arxiv.org/pdf/2304.13031v1.pdf) [`det`; [PyTorch](https://github.com/AIR-DISCOVER/DQS3D)]

  - [Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction](https://arxiv.org/pdf/2304.00967v1.pdf) [`det`; [Github](https://github.com/Sense-X/HoP)]

  - [Robo3D: Towards Robust and Reliable 3D Perception against Corruptions](https://arxiv.org/pdf/2303.17597.pdf) [`seg`, `det`; [Github](https://github.com/ldkong1205/Robo3D)]

  - [Clustering based Point Cloud Representation Learning for 3D Analysis](https://arxiv.org/pdf/2307.14605.pdf) [`seg`, `det`; [PyTorch](https://github.com/FengZicai/Cluster3Dseg)]

  - [MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception](https://arxiv.org/pdf/2211.10593.pdf) [`seg`, `det`]

  - [DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds](https://arxiv.org/pdf/2306.06023.pdf) [`det`; [Github](https://github.com/PJLab-ADG/DetZero)]

  - [Efficient 3D Semantic Segmentation with Superpoint Transformer](https://arxiv.org/pdf/2306.08045.pdf) [`seg`; [PyTorch](https://github.com/drprojects/superpoint_transformer)]

  - [Generalized Few-Shot Point Cloud Segmentation Via Geometric Words](https://arxiv.org/pdf/2309.11222.pdf) [`seg`]

  - [Rethinking Range View Representation for LiDAR Segmentation](https://arxiv.org/pdf/2303.05367v1.pdf) [`seg`]

  - [Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2308.09314.pdf) [`seg`; [Github](https://github.com/AllenXiangX/Retro-FPN)]

  - [Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2308.11166.pdf) [`seg`; [PyTorch](https://github.com/SmiletoE/HPAL)]

  - [PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning](https://arxiv.org/pdf/2211.11682.pdf) [`pre-training`; [Github](https://github.com/yangyangyang127/PointCLIP_V2)]

  - [Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models](https://arxiv.org/pdf/2307.14971.pdf) [`pre-training`; [PyTorch](https://github.com/wangzy22/TAP)]

  - [Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models](https://arxiv.org/pdf/2304.07221.pdf) [`pre-training`; [PyTorch](https://github.com/zyh16143998882/ICCV23-IDPT)]

  - [CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training](https://arxiv.org/pdf/2210.01055.pdf) [`pre-training`; [PyTorch](https://github.com/tyhuang0428/CLIP2Point)]

  - [Implicit Autoencoder for Point Cloud Self-supervised Representation Learning](https://arxiv.org/pdf/2201.00785.pdf) [`self-supervised`; [PyTorch](https://github.com/SimingYan/IAE)]

  - [Ponder: Point Cloud Pre-training via Neural Rendering](https://arxiv.org/pdf/2301.00157v1.pdf) [`self-supervised`; [Github](https://dihuangdh.github.io/ponder/)]

  - [You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation](https://arxiv.org/pdf/2304.11762v1.pdf) [`annotation`; [PyTorch](https://github.com/nerminsamet/seedal)]

  - [Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning](https://arxiv.org/pdf/2308.16481.pdf) [`registration`]

  - [RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration](https://arxiv.org/pdf/2303.12384.pdf) [`registration`; [PyTorch](https://github.com/IRMVLab/RegFormer)]

  - [PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration](https://arxiv.org/pdf/2308.04782.pdf) [`registration`]

  - [Density-invariant Features for Distant Point Cloud Registration](https://arxiv.org/pdf/2307.09788.pdf) [`registration`; [PyTorch](https://github.com/liuQuan98/GCL)]

  - [AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration](https://arxiv.org/pdf/2309.11170.pdf) [`generation`, `registration`]

  - [GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation](https://arxiv.org/pdf/2211.16762.pdf) [`reconstruction`; [PyTorch](https://github.com/rsy6318/GeoUDF)]

  - [P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds](https://arxiv.org/pdf/2307.14726.pdf) [`completion`; [PyTorch](https://github.com/CuiRuikai/Partial2Complete)]

  - [Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly](https://arxiv.org/pdf/2309.06810v1.pdf) [`assembly`; [Project](https://crtie.github.io/SE-3-part-assembly/)]

  - [TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses](https://arxiv.org/pdf/2306.05888.pdf) [`tracking`]

- CVPR

  - [FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation](https://arxiv.org/pdf/2307.01492.pdf) [`semantic occupancy prediction`; [Github](https://github.com/NVlabs/FB-BEV); CVPRW]

  - [Planning-oriented Autonomous Driving](https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Planning-Oriented_Autonomous_Driving_CVPR_2023_paper.pdf) [`autonomous driving`; [PyTorch](https://github.com/OpenDriveLab/UniAD)]

  - [MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences](https://arxiv.org/pdf/2306.03206.pdf) [`det`]

  - [FrustumFormer: Adaptive Instance-aware Resampling for Multi-view 3D Detection](https://arxiv.org/pdf/2301.04467v1.pdf) [`det`]

  - [Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild](https://arxiv.org/pdf/2207.10660.pdf) [`det`; [PyTorch](https://github.com/facebookresearch/omni3d)]

  - [BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection](https://arxiv.org/pdf/2303.08498v1.pdf) [`det`; [PyTorch](https://github.com/ADLab-AutoDrive/BEVHeight)]

  - [TorchSparse++: Efficient Point Cloud Engine](https://openaccess.thecvf.com/content/CVPR2023W/WAD/papers/Tang_TorchSparse_Efficient_Point_Cloud_Engine_CVPRW_2023_paper.pdf) [`engine`; [PyTorch](https://github.com/mit-han-lab/torchsparse)]

  - [Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection](https://arxiv.org/pdf/2306.01438.pdf) [`det`; [PyTorch](https://github.com/JessieW0806/Bi-LRFusion)]

  - [NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization](https://arxiv.org/pdf/2305.17763.pdf) [`localization`]

  - [Long Range Pooling for 3D Large-Scale Scene Understanding](https://arxiv.org/pdf/2301.06962v1.pdf) [`seg`]

  - [PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models](https://arxiv.org/pdf/2212.01558.pdf) [`seg`]

  - [GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds](https://arxiv.org/pdf/2305.16404.pdf) [`seg`; [PyTorch](https://github.com/vLAR-group/GrowSP)]

  - [MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency](https://arxiv.org/pdf/2212.09948.pdf) [`seg`, `det`]

  - [3D Registration with Maximal Cliques](https://arxiv.org/pdf/2305.10854.pdf) [`registration`; [Github](https://github.com/zhangxy0517/3D-Registration-with-Maximal-Cliques)]

  - [DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection](https://arxiv.org/pdf/2305.15219.pdf) [`det`; CVPRW]

  - [GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training](https://arxiv.org/pdf/2305.08808.pdf) [`self-supervised`; [Github](https://github.com/Tsinghua-MARS-Lab/GeoMAE)]

  - [PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer](https://arxiv.org/pdf/2305.06621.pdf) [`det`; [Github](https://github.com/Nightmare-n/PVT-SSD)]

  - [SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds](https://arxiv.org/pdf/2305.05873.pdf) [`normal estimation`; [PyTorch](https://github.com/LeoQLi/SHS-Net)]

  - [Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving](https://arxiv.org/pdf/2305.06242.pdf) [`autonomous driving`; [PyTorch](https://github.com/OpenDriveLab/ThinkTwice)]

  - [Self-supervised Pre-training with Masked Shape Prediction for 3D Scene Understanding](https://arxiv.org/pdf/2305.05026.pdf) [`self-supervised`]

  - [PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos](https://arxiv.org/pdf/2305.04075.pdf) [`self-supervised`]

  - [Point2Vec for Self-Supervised Representation Learning on Point Clouds](https://arxiv.org/pdf/2303.16570.pdf) [`self-supervised`; [Project](https://point2vec.ka.codes); CVPRW]

  - [PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds](https://arxiv.org/pdf/2305.04925.pdf) [`det`]

  - [Self-Supervised 3D Scene Flow Estimation Guided by Superpoints](https://arxiv.org/pdf/2305.02528.pdf) [`scene flow`; [Github](https://github.com/supersyq/SPFlowNet)]

  - [Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions](https://arxiv.org/pdf/2304.11846v1.pdf) [`upsampling`; [PyTorch](https://github.com/yunhe20/Grad-PU)]

  - [SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation](https://arxiv.org/pdf/2212.04493.pdf) [`completion`, `reconstruction`, `generation`; [Github](https://github.com/yccyenchicheng/SDFusion)]

  - [Fast Point Cloud Generation with Straight Flows](https://arxiv.org/pdf/2212.01747.pdf) [`generation`]

  - [LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation](https://arxiv.org/pdf/2304.11379v1.pdf) [`seg`; [Github](https://github.com/songw-zju/LiDAR2Map)]

  - [Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection](https://arxiv.org/pdf/2304.09446v1.pdf) [`det`; [PyTorch](https://github.com/WoodwindHu/DTS)]

  - [Curricular Object Manipulation in LiDAR-based Object Detection](https://arxiv.org/pdf/2304.04248v1.pdf) [`det`; [PyTorch](https://github.com/ZZY816/COM)]

  - [Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation](https://arxiv.org/pdf/2304.02991v1.pdf) [CVPRW]

  - [Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection](https://arxiv.org/pdf/2304.01464v1.pdf) [`det`; [PyTorch](https://github.com/azhuantou/HSSDA)]

  - [IterativePFN: True Iterative Point Cloud Filtering](https://arxiv.org/pdf/2304.01529v1.pdf) [`filtering`; [Github](https://github.com/ddsediri/IterativePFN)]

  - [Robust Outlier Rejection for 3D Registration with Variational Bayes](https://arxiv.org/pdf/2304.01514v1.pdf) [`registration`; [Github](https://github.com/Jiang-HB/VBReg)]

  - [Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting](https://arxiv.org/pdf/2304.00467v1.pdf) [`registration`; [PyTorch](https://github.com/WHU-USI3DV/SGHR)]

  - [3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds](https://arxiv.org/pdf/2304.00690v1.pdf) [`seg`; [PyTorch](https://github.com/xiaoaoran/SemanticSTF)]

  - [RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving](https://arxiv.org/pdf/2301.10222.pdf) [`seg`; [Github](https://github.com/valeoai/rangevit)]

  - [Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving](https://arxiv.org/pdf/2303.17297.pdf) [`det`]

  - [NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud](https://arxiv.org/pdf/2303.16465.pdf) [`parametric curve`; [Project](https://dongdu3.github.io/projects/2023/NerVE/)]

  - [LinK: Linear Kernel for LiDAR-based 3D Perception](https://arxiv.org/pdf/2303.16094.pdf) [`det`, `seg`; [Github](https://github.com/MCG-NJU/LinK)]

  - [NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation](https://arxiv.org/pdf/2303.15126.pdf) [`interpolation`; [Github](https://github.com/ispc-lab/NeuralPCI)]

  - [UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View](https://arxiv.org/pdf/2303.15083.pdf) [`det`; [PyTorch](https://github.com/megvii-research/CVPR2023-UniDistill)]

  - [3D Video Object Detection with Learnable Object-Centric Global Optimization](https://arxiv.org/pdf/2303.15416.pdf) [`det`; [Github](https://github.com/jiaweihe1996/BA-Det)]

  - [EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision](https://arxiv.org/pdf/2303.15440.pdf) [`seg`; [PyTorch](https://github.com/JiahuiLei/EFEM)]

  - [Collaboration Helps Camera Overtake LiDAR in 3D Detection](https://arxiv.org/pdf/2303.13560v1.pdf) [`det`; [Github](https://github.com/MediaBrain-SJTU/CoCa3D)]

  - [Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration](https://arxiv.org/pdf/2303.13290v1.pdf) [`registration`; [Github](https://github.com/gfmei/UDPReg)]

  - [MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer](https://arxiv.org/pdf/2303.13018v1.pdf) [`det`]

  - [FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer](https://arxiv.org/pdf/2301.08739.pdf) [`det`]

  - [OcTr: Octree-based Transformer for 3D Object Detection](https://arxiv.org/pdf/2303.12621v1.pdf) [`det`]

  - [Spherical Transformer for LiDAR-based 3D Recognition](https://arxiv.org/pdf/2303.12766v1.pdf) [`seg`, `det`; [PyTorch](https://github.com/dvlab-research/SphereFormer)]

  - [Novel Class Discovery for 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2303.11610v1.pdf) [`seg`; [PyTorch](https://github.com/LuigiRiz/NOPS)]

  - [3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process](https://arxiv.org/pdf/2303.10406v1.pdf) [`generation`; [PyTorch](https://github.com/colorful-liyu/3DQD)]

  - [Learning 3D Scene Priors with 2D Supervision](https://arxiv.org/pdf/2211.14157.pdf) [`layout`, `shape`; [Project](https://yinyunie.github.io/sceneprior-page/)]

  - [Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching](https://arxiv.org/pdf/2303.10971v1.pdf) [`non-rigid matching`; [PyTorch](https://github.com/dongliangcao/Self-Supervised-Multimodal-Shape-Matching)]

  - [Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2303.11203v1.pdf) [`seg`; [Github](https://github.com/l1997i/lim3d/)]

  - [CAPE: Camera View Position Embedding for Multi-View 3D Object Detection](https://arxiv.org/pdf/2303.10209v1.pdf) [`det`; [Github](https://github.com/kaixinbear/CAPE)]

  - [Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving](https://arxiv.org/pdf/2303.11040v1.pdf) [`det`; [Github](https://github.com/kkkcx/3D_Corruptions_AD)]

  - [AeDet: Azimuth-invariant Multi-view 3D Object Detection](https://arxiv.org/pdf/2211.12501.pdf) [`det`; [PyTorch](https://github.com/fcjian/AeDet)]

  - [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://arxiv.org/pdf/2303.11301v1.pdf) [`det`, `tracking`; [PyTorch](https://github.com/dvlab-research/VoxelNeXt)]

  - [Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking](https://arxiv.org/pdf/2302.03802.pdf) [`tracking`; [Github](https://github.com/TRI-ML/PF-Track)]

  - [Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration](https://arxiv.org/pdf/2303.09950v1.pdf) [`non-rigid registration`; [Github](https://github.com/qinzheng93/GraphSCNet)]

  - [PEAL: Prior-Embedded Explicit Attention Learning for Low-Overlap Point Cloud Registration](https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_PEAL_Prior-Embedded_Explicit_Attention_Learning_for_Low-Overlap_Point_Cloud_Registration_CVPR_2023_paper.pdf) [`registration`; [Github](https://github.com/Gardlin/PEAL)]

  - [BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration](https://openaccess.thecvf.com/content/CVPR2023/papers/Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper.pdf) [`registration`; [Github](https://github.com/aosheng1996/BUFFER)]

  - [TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving](https://arxiv.org/pdf/2303.09998v1.pdf) [`autonomous driving`; [Github](https://github.com/MediaBrain-SJTU/TBP-Former)]

  - [MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving](https://arxiv.org/pdf/2303.08600v1.pdf) [`seg`; [PyTorch](https://github.com/jialeli1/lidarseg3d)]

  - [MSF: Motion-guided Sequential Fusion for Efficient 3D Object Detection from Point Cloud Sequences](https://arxiv.org/pdf/2303.08316v1.pdf) [`det`; [Github](https://github.com/skyhehe123/MSF)]

  - [Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency](https://arxiv.org/pdf/2303.08686v1.pdf) [`det`; [Github](https://github.com/weakmono3d/weakmono3d)]

  - [Rotation-Invariant Transformer for Point Cloud Matching](https://arxiv.org/pdf/2303.08231v1.pdf) [`matching`]

  - [Point2Pix: Photo-Realistic Point Cloud Rendering via Neural Radiance Fields](https://arxiv.org/pdf/2303.16482.pdf) [`rendering`]

  - [Frequency-Modulated Point Cloud Rendering with Easy Editing](https://arxiv.org/pdf/2303.07596v1.pdf) [`rendering`; [PyTorch](https://github.com/yizhangphd/FreqPCR)]

  - [Meta Architecure for Point Cloud Analysis](https://arxiv.org/pdf/2211.14462.pdf) [`seg`, `cls`; [PyTorch](https://github.com/linhaojia13/PointMetaBase)]

  - [ULIP: Learning Unified Representation of Language, Image and Point Cloud for 3D Understanding](https://arxiv.org/pdf/2212.05171.pdf) [`cls`; [Github](https://github.com/salesforce/ULIP)]

  - [Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis](https://arxiv.org/pdf/2303.08134v1.pdf) [`cls`, `seg`, `det`; [PyTorch](https://github.com/ZrrSkywalker/Point-NN)]

  - [PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection](https://arxiv.org/pdf/2303.08129v1.pdf) [`det`; [PyTorch](https://github.com/BLVLab/PiMAE)]

  - [SCPNet: Semantic Scene Completion on Point Cloud](https://arxiv.org/pdf/2303.06884v1.pdf) [`semantic scene completion`]

  - [Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection](https://arxiv.org/pdf/2303.06880v1.pdf) [`det`; [Github](https://github.com/PJLab-ADG/3DTrans)]

  - [Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection](https://arxiv.org/pdf/2303.05886v1.pdf) [`det`; [Github](https://github.com/PJLab-ADG/3DTrans)]

  - [LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion](https://arxiv.org/pdf/2303.03595v1.pdf) [`det`; [Github](https://github.com/sankin97/LoGoNet)]

  - [X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection](https://arxiv.org/pdf/2303.02203v1.pdf) [`det`]

  - [Virtual Sparse Convolution for Multimodal 3D Object Detection](https://arxiv.org/pdf/2303.02314v1.pdf) [`det`; [PyTorch](https://github.com/hailanyi/VirConv)]

  - [Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly](https://arxiv.org/pdf/2303.01999v1.pdf) [`reconstruction`]

  - [ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion](https://arxiv.org/pdf/2303.01979v1.pdf) [`completion`; [Github](https://github.com/Sangminhong/ACL-SPC_PyTorch)]

  - [PointCert: Point Cloud Classification with Deterministic Certified Robustness Guarantees](https://arxiv.org/pdf/2303.01959v1.pdf) [`cls`]

  - [Towards Domain Generalization for Multi-view 3D Object Detection in Bird-Eye-View](https://arxiv.org/pdf/2303.01686v1.pdf) [`det`]

  - [Neural Intrinsic Embedding for Non-rigid Point Cloud Matching](https://arxiv.org/pdf/2303.01038v1.pdf) [`non-rigid matching`]

  - [SBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution](https://arxiv.org/pdf/2303.00246v1.pdf) [`seg`]

  - [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://arxiv.org/pdf/2303.00601.pdf) [`anomaly detection`; [Github](https://github.com/nomewang/M3DM)]

  - [ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer](https://arxiv.org/pdf/2302.14435v1.pdf) [`completion`; [PyTorch](https://github.com/I2-Multimedia-Lab/ProxyFormer)]

  - [Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting](https://arxiv.org/pdf/2302.13130.pdf) [`autonomous driving`; [PyTorch](https://github.com/tarashakhurana/4d-occ-forecasting)]

  - [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://arxiv.org/pdf/2302.12251.pdf) [`autonomous driving`; [Github](https://github.com/NVlabs/VoxFormer)]

  - [Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction](https://arxiv.org/pdf/2302.07817.pdf) [`autonomous driving`; [PyTorch](https://github.com/wzzheng/TPVFormer)]

  - [CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP](https://arxiv.org/pdf/2301.04926v1.pdf) [`pre-training`]

  - [CLIP^2: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data](https://arxiv.org/pdf/2303.12417v1.pdf) [`pre-training`]

  - [ConQueR: Query Contrast Voxel-DETR for 3D Object Detection](https://arxiv.org/pdf/2212.07289.pdf) [`det`; [Github](https://github.com/poodarchu/ConQueR)]

  - [Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders](https://arxiv.org/pdf/2212.06785.pdf) [`pre-training`; [Github](https://github.com/ZrrSkywalker/I2P-MAE)]

  - [GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds](https://arxiv.org/pdf/2212.03010.pdf) [`pre-training`; [Github](https://github.com/Nightmare-n/GD-MAE)]

  - [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://arxiv.org/pdf/2211.10439.pdf) [`autonomous driving`]

  - [MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection](https://arxiv.org/pdf/2209.03102.pdf) [`det`; [Github](https://github.com/SxJyJay/MSMDFusion)]

  - [DETRs with Hybrid Matching](https://arxiv.org/pdf/2207.13080.pdf) [`det`; [Github](https://github.com/HDETR)]

  - [LaserMix for Semi-Supervised LiDAR Semantic Segmentation](https://arxiv.org/pdf/2207.00026.pdf) [`seg`; [PyTorch](https://github.com/ldkong1205/LaserMix)]

  - [PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection](https://arxiv.org/pdf/2205.11098.pdf) [`det`; [Github](https://github.com/RunpeiDong/PointDistiller)]

  - [Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection](https://arxiv.org/pdf/2301.02371v1.pdf) [`lane det`]

  - [PLA: Language-Driven Open-Vocabulary 3D Scene Understanding](https://arxiv.org/pdf/2211.16312.pdf) [`open-vocabulary`; [Github](https://github.com/CVMI-Lab/PLA)]

- AAAI

  - [Context-Aware Transformer for 3D Point Cloud Automatic Annotation](https://arxiv.org/pdf/2303.14893.pdf) [`annotation`]

  - [GAM : Gradient Attention Module of Optimization for Point Clouds Analysis](https://arxiv.org/pdf/2303.10543v1.pdf) [`seg`, `det`, `cls`; [PyTorch](https://github.com/hht1996ok/GAM)]

  - [Parametric Surface Constrained Upsampler Network for Point Cloud](https://arxiv.org/pdf/2303.08240v1.pdf) [`upsampling`; [PyTorch](https://github.com/corecai163/PSCU)]

  - [CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame](https://arxiv.org/pdf/2303.03101v1.pdf) [`rotation invariance`]

  - [PUPS: Point Cloud Unified Panoptic Segmentation](https://arxiv.org/pdf/2302.06185.pdf) [`seg`]

  - [StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection](https://arxiv.org/pdf/2301.01615v1.pdf) [`det`]

  - [Rethinking Rotation Invariance with Point Cloud Registration](https://arxiv.org/pdf/2301.00149v1.pdf) [`cls`, `seg`, `retrieval`; [PyTorch](https://github.com/Crane-YU/rethink_rotation)]

  - [SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud](https://arxiv.org/pdf/2212.02845.pdf) [`det`; [Github](https://github.com/yinjunbo/SSDA3D)]

  - [CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection](https://arxiv.org/pdf/2212.00244.pdf) [`det`; [Github](https://github.com/4DVLab/CL3D)]

  - [MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection](https://arxiv.org/pdf/2212.00442.pdf) [`det`; [PyTorch](https://github.com/HYjhkoh/MGTANet)]

  - [Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection](https://arxiv.org/pdf/2211.16779.pdf) [`det`; [Github](https://github.com/rockywind/ADD)]

  - [NeAF: Learning Neural Angle Fields for Point Normal Estimation](https://arxiv.org/pdf/2211.16869.pdf) [`normal estimation`; [PyTorch](https://github.com/lisj575/NeAF/)]

  - [Superpoint Transformer for 3D Scene Instance Segmentation](https://arxiv.org/pdf/2211.15766.pdf) [`seg`; [PyTorch](https://github.com/sunjiahao1999/SPFormer)]

  - [Language-Assisted 3D Feature Learning for Semantic Scene Understanding](https://arxiv.org/pdf/2211.14091.pdf) [`det`, `seg`; [Github](https://github.com/Asterisci/Language-Assisted-3D)]

  - [CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion](https://arxiv.org/pdf/2211.13702.pdf) [`semantic scene completion`; [Github](https://github.com/JinfengX/CasFusionNet)]

  - [SEFormer: Structure Embedding Transformer for 3D Object Detection](https://arxiv.org/pdf/2209.01745.pdf) [`det`]

  - [Transformation-Equivariant 3D Object Detection for Autonomous Driving](https://arxiv.org/pdf/2211.11962.pdf) [`det`]

  - [PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples](https://arxiv.org/pdf/2211.12294.pdf) [`completion`]

  - [GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds](https://arxiv.org/pdf/2211.10927.pdf) [`tracking`; [Github](https://github.com/haooozi/GLT-T)]

  - [Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics](https://arxiv.org/pdf/2211.10580.pdf) [`normal estimation`]

  - [A Simple Baseline for Multi-Camera 3D Object Detection](https://arxiv.org/pdf/2208.10035.pdf) [`det`; [Github](https://github.com/zhangyp15/SimMOD)]

  - [PolarFormer: Multi-camera 3D Object Detection with Polar Transformer](https://arxiv.org/pdf/2206.15398.pdf) [`det`; [PyTorch](https://github.com/fudan-zvg/PolarFormer)]

  - [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://arxiv.org/pdf/2206.10092.pdf) [`det`; [PyTorch](https://github.com/Megvii-BaseDetection/BEVDepth)]

  - [BEVStereo: Enhancing Depth Estimation in Multi-view 3D Object Detection with Dynamic Temporal Stereo](https://arxiv.org/pdf/2209.10248.pdf) [`det`; [PyTorch](https://github.com/Megvii-BaseDetection/BEVStereo)]

- Others

  - [PointGPT: Auto-regressively Generative Pre-training from Point Clouds](https://arxiv.org/pdf/2305.11487.pdf) [`pre-training`; [Github](https://github.com/CGuangyan-BIT/PointGPT); NeurIPS]

  - [Segment Anything in 3D with NeRFs](https://arxiv.org/pdf/2304.12308v1.pdf) [`seg`; [Github](https://github.com/Jumpat/SegmentAnythingin3D); NeurIPS]

  - [SAM3D: Segment Anything in 3D Scenes](https://arxiv.org/pdf/2306.03908.pdf) [`seg`; [PyTorch](https://github.com/Pointcept/SegmentAnything3D); NeurIPS]

  - [Segment Any Point Cloud Sequences by Distilling Vision Foundation Models](https://arxiv.org/pdf/2306.09347.pdf) [`seg`; [Github](https://github.com/youquanl/Segment-Any-Point-Cloud); NeurIPS]

  - [Real3D-AD: A Dataset of Point Cloud Anomaly Detection](https://arxiv.org/pdf/2309.13226.pdf) [`det`; [Github](https://github.com/M-3LAB/Real3D-AD); NeurIPS]

  - [AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset](https://arxiv.org/pdf/2306.00612.pdf) [`pre-training`; [PyTorch](https://github.com/PJLab-ADG/3DTrans); NeurIPS]

  - [Explore In-Context Learning for 3D Point Cloud Understanding](https://arxiv.org/pdf/2306.08659.pdf) [`in-context learning`; [Github](https://github.com/fanglaosi/Point-In-Context); NeurIPS]

  - [E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning](https://arxiv.org/pdf/2311.18433.pdf) [`registration`; NeurIPS]

  - [SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation](https://arxiv.org/pdf/2310.17359.pdf) [`pose estimation`; [PyTorch](https://github.com/Jiang-HB/DiffusionReg); NeurIPS]

  - [Banana: Banach Fixed-Point Network for Pointcloud Segmentation with Inter-Part Equivariance](https://arxiv.org/pdf/2305.16314.pdf) [`seg`; NeurIPS]

  - [All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation](https://arxiv.org/pdf/2305.15832.pdf) [`seg`; NeurIPS]

  - [DiffComplete: Diffusion-based Generative 3D Shape Completion](https://arxiv.org/pdf/2306.16329.pdf) [`completion`; [Project](https://ruihangchu.com/diffcomplete.html); NeurIPS]

  - [DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking](https://arxiv.org/pdf/2303.16628.pdf) [`det`, `tracking`; [Github](https://github.com/SmartBot-PJLab/DORT); CoRL]

  - [SUIT: Learning Significance-guided Information for 3D Temporal Detection](https://arxiv.org/pdf/2307.01807.pdf) [`det`; IROS]

  - [Sparse Dense Fusion for 3D Object Detection](https://arxiv.org/pdf/2304.04179v1.pdf) [`det`; IROS]

  - [PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation](https://arxiv.org/pdf/2306.15348.pdf) [`seg`; [PyTorch](https://github.com/Jieqianyu/PANet); IROS]

  - [SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion](https://arxiv.org/pdf/2306.15349.pdf) [`semantic scene completion`; [PyTorch](https://github.com/Jieqianyu/SSC-RS); IROS]

  - [ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data](https://arxiv.org/pdf/2303.04351v1.pdf) [`seg`; [Github](https://github.com/nubot-nudt/ElC-OIS); IROS]

  - [InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data](https://arxiv.org/pdf/2303.03909v1.pdf) [`seg`; [Github](https://github.com/nubot-nudt/InsMOS); IROS]

  - [Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning](https://arxiv.org/pdf/2306.11406.pdf) [`analysis`; ICML]

  - [VectorMapNet: End-to-end Vectorized HD Map Learning](https://arxiv.org/pdf/2206.08920.pdf) [`autonomous driving`; [Github](https://github.com/Tsinghua-MARS-Lab/vectormapnet); ICML]

  - [STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking](https://arxiv.org/pdf/2306.17440.pdf) [`det`; RAL]

  - [Concavity-Induced Distance for Unoriented Point Cloud Decomposition](https://arxiv.org/pdf/2306.11051.pdf) [`analysis`; [Project](https://ai4ce.github.io/CID/); RAL]

  - [Energy-based Detection of Adverse Weather Effects in LiDAR Data](https://arxiv.org/pdf/2305.16129.pdf) [`autonomous driving`; [Github](https://github.com/aldipiroli/semantic_spray_dataset); RAL]

  - [Multi-modal Streaming 3D Object Detection](https://arxiv.org/pdf/2209.04966.pdf) [`det`; RAL]

  - [You Only Label Once: 3D Box Adaptation From Point Cloud to Image With Semi-Supervised Learning](https://arxiv.org/pdf/2211.09302.pdf) [`det`; RAL]

  - [Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2305.14335.pdf) [`seg`; [Github](https://github.com/heshuting555/PAP-FZS3D); TIP]

  - [APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification](https://arxiv.org/pdf/2205.00847.pdf) [`cls`; [PyTorch](https://github.com/MCG-NJU/APP-Net); TIP]

  - [OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection](https://arxiv.org/pdf/2212.10049.pdf) [`det`; [Github](https://github.com/mrsempress/OBMO); TIP]

  - [OctFormer: Octree-based Transformers for 3D Point Clouds](https://arxiv.org/pdf/2305.03045.pdf) [`seg`, `det`; [Github](https://github.com/octree-nn/octformer); TOG]

  - [Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking](https://arxiv.org/pdf/2209.06395.pdf) [`tracking`; TNNLS]

  - [Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction](https://arxiv.org/pdf/2112.09385.pdf) [`registration`; [Github](https://github.com/CGuangyan-BIT/DIT); TNNLS]

  - [CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data](https://arxiv.org/pdf/2302.01665.pdf) [`place recognition`; [Github](https://github.com/BIT-MJY/CVTNet); TII]

  - [PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View](https://arxiv.org/pdf/2306.10761.pdf) [`autonomous driving`; [PyTorch](https://github.com/EdwardLeeLPZ/PowerBEV); IJCAI]

  - [APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction](https://arxiv.org/pdf/2305.02893.pdf) [`registration`; [Github](https://github.com/liuQuan98/APR); IJCAI]

  - [OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking](https://arxiv.org/pdf/2304.11584v1.pdf) [`tracking`; IJCAI]

  - [Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training](https://arxiv.org/pdf/2302.14007.pdf) [`pre-training`; IJCAI]

  - [A Closer Look at Few-Shot 3D Point Cloud Classification](https://arxiv.org/pdf/2303.18210v1.pdf) [`cls`; [Github](https://github.com/cgye96/A_Closer_Look_At_3DFSL); IJCV]

  - [PointNorm: Normalization is All You Need for Point Cloud Analysis](https://arxiv.org/pdf/2207.06324.pdf) [`cls`, `seg`; [Github](https://github.com/ShenZheng2000/PointNorm-for-Point-Cloud-Analysis); IJCNN]

  - [HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching](https://arxiv.org/pdf/2303.16526.pdf) [`registration`; [Github](https://github.com/liyih/HybridPoint); ICME]

  - [Variational Relational Point Completion Network for Robust 3D Classification](https://arxiv.org/pdf/2304.09131v1.pdf) [`completion`; TPAMI]

  - [CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm](https://arxiv.org/pdf/2207.05359.pdf) [`completion`; TPAMI]

  - [Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization](https://arxiv.org/pdf/2206.03410.pdf) [`registration`, `non-rigid`; [Github](https://github.com/yaoyx689/AMM_NRR); TPAMI]

  - [RoReg: Pairwise Point Cloud Registration with Oriented Descriptors and Local Rotations](https://ieeexplore.ieee.org/abstract/document/10044259) [`registration`; [PyTorch](https://github.com/HpWang-whu/RoReg); TPAMI]

  - [AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers](https://arxiv.org/pdf/2301.04545v1.pdf) [`completion`; TPAMI]

  - [Super Sparse 3D Object Detection](https://arxiv.org/pdf/2301.02562.pdf) [`det`; [PyTorch](https://github.com/tusen-ai/SST); TPAMI]

  - [AGConv: Adaptive Graph Convolution on 3D Point Clouds](https://arxiv.org/pdf/2206.04665.pdf) [`cls`, `seg`; [PyTorch](https://github.com/hrzhou2/AdaptConv-master); TPAMI]

  - [Analogy-Forming Transformers for Few-Shot 3D Parsing](https://arxiv.org/pdf/2304.14382v1.pdf) [`seg`; [Project](https://analogicalnets.github.io); ICLR]

  - [Joint 2D-3D multi-task scene understanding on Cityscapes-3D: 3D detection, segmentation, and depth estimation](https://openreview.net/pdf?id=-CwPopPJda) [`seg`, `det`, `depth estimation`; [PyTorch](https://github.com/prismformore/Multi-Task-Transformer/tree/main/TaskPrompter); ICLR]

  - [CircNet: Meshing 3D Point Clouds with Circumcenter Detection](https://arxiv.org/pdf/2301.09253.pdf) [`triangulation`; [Github](https://github.com/Ruitao-L/CircNet); ICLR]

  - [BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection](https://arxiv.org/pdf/2211.09386.pdf) [`det`; [Github](https://github.com/zehuichen123/BEVDistill); ICLR]

  - [Bidirectional Propagation for Cross-Modal 3D Object Detection](https://arxiv.org/pdf/2301.09077.pdf) [`det`; [PyTorch](https://github.com/Eaphan/BiProDet); ICLR]

  - [Exploring Active 3D Object Detection from a Generalization Perspective](https://arxiv.org/pdf/2301.09249.pdf) [`det`; [PyTorch](https://github.com/Luoyadan/CRB-active-3Ddet); ICLR]

  - [DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection](https://arxiv.org/pdf/2207.10909.pdf) [`det`; ICLR]

  - [DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection](https://arxiv.org/pdf/2303.05079v1.pdf) [`det`; [Github](https://github.com/hust-jy/DDS3D); ICRA]

  - [Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion](https://arxiv.org/pdf/2303.01804v1.pdf) [`completion`; ICRA]

  - [A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation](https://arxiv.org/pdf/2302.14511v1.pdf) [`registration`; ICRA]

  - [LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR](https://arxiv.org/pdf/2302.14052.pdf) [`completion`; [PyTorch](https://github.com/AIR-DISCOVER/LODE); ICRA]

  - [DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving](https://arxiv.org/pdf/2302.13577.pdf) [`det`; ICRA]

  - [Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention](https://arxiv.org/pdf/2302.10501.pdf) [`seg`; ICRA]

  - [MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion](https://arxiv.org/pdf/2302.10511.pdf) [`det`; ICRA]

  - [MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts](https://arxiv.org/pdf/2302.10549.pdf) [`det`; ICRA]

  - [SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping](https://arxiv.org/pdf/2301.07213v1.pdf) [`completion`; [PyTorch](https://github.com/vanhalen42/SCARP); ICRA]

  - [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://arxiv.org/pdf/2205.13542.pdf) [`autonomous driving`; [PyTorch](https://github.com/mit-han-lab/bevfusion); ICRA]

  - [CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection](https://arxiv.org/pdf/2209.13507.pdf) [`det`; [Github](https://github.com/sty61010/CrossDTR); ICRA]

  - [ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation](https://arxiv.org/pdf/2111.15242.pdf) [`seg`, `unsupervised domain adaptation`; [Github](https://github.com/ldkong1205/ConDA); ICRA]

  - [Mask3D: Mask Transformer for 3D Semantic Instance Segmentation](https://arxiv.org/pdf/2210.03105.pdf) [`seg`; ICRA]

  - [STD: Stable Triangle Descriptor for 3D place recognition](https://arxiv.org/pdf/2209.12435.pdf) [`place recognition`; [Github](https://github.com/hku-mars/STD); ICRA]

  - [MonoEdge: Monocular 3D Object Detection Using Local Perspectives](https://arxiv.org/pdf/2301.01802v1.pdf) [`det`; WACV]

  - [Far3Det: Towards Far-Field 3D Detection](https://arxiv.org/pdf/2211.13858.pdf) [`det`; WACV]

  - [Centroid Distance Keypoint Detector for Colored Point Clouds](https://arxiv.org/pdf/2210.01298.pdf) [`keypoint`; [Github](https://github.com/UCR-Robotics/CED_Detector); WACV]

  - [NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds](https://arxiv.org/pdf/2207.09978.pdf) [`seg`; [Project](https://neuralbf.github.io); WACV]

  - [Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors](https://arxiv.org/pdf/2202.11660.pdf) [`anomaly detection`; WACV]

  - [SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification](https://arxiv.org/pdf/2305.09160.pdf) [`cls`; [PyTorch](https://github.com/SiyuanHuang95/SUG); ACM MM]

  - [Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge](https://arxiv.org/pdf/2302.09221.pdf) [`det`; ACM MM]

  - [TR3D: Towards Real-Time Indoor 3D Object Detection](https://arxiv.org/pdf/2302.02858.pdf) [`det`; [PyTorch](https://github.com/SamsungLabs/tr3d); ICIP]

  - [MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection](https://arxiv.org/pdf/2304.02431v1.pdf) [`det`; [PyTorch](https://github.com/darrenjkt/MS3D); TITS]

- arXiv

  - [DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation](https://arxiv.org/pdf/2307.01831.pdf) [`generation`; [Github](https://github.com/DiT-3D/DiT-3D)]

  - [Frame Fusion with Vehicle Motion Prediction for 3D Object Detection](https://arxiv.org/pdf/2306.10699.pdf) [`det`]

  - [UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering](https://arxiv.org/pdf/2306.09117.pdf) [`occupancy prediction`]

  - [SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model](https://arxiv.org/pdf/2306.02245.pdf) [`det`; [PyTorch](https://github.com/DYZhang09/SAM3D)]

  - [Collect-and-Distribute Transformer for 3D Point Cloud Analysis](https://arxiv.org/pdf/2306.01257.pdf) [`cls`, `seg`; [PyTorch](https://github.com/haibo-qiu/CDFormer)]

  - [BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy](https://arxiv.org/pdf/2305.16829.pdf) [`det`]

  - [SAD: Segment Any RGBD](https://arxiv.org/pdf/2305.14207.pdf) [`seg`; [PyTorch](https://github.com/Jun-CEN/SegmentAnyRGBD)]

  - [Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection](https://arxiv.org/pdf/2305.12853.pdf) [`augmentation`]

  - [Multi-Modal 3D Object Detection by Box Matching](https://arxiv.org/pdf/2305.07713.pdf) [`det`; [Github](https://github.com/happinesslz/FBMNet)]

  - [ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds](https://arxiv.org/pdf/2304.12589v1.pdf) [`scene motion`]

  - [MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation](https://arxiv.org/pdf/2304.09801v1.pdf) [`det`, `seg`; [Github](https://github.com/ChongjianGE/MetaBEV)]

  - [3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining](https://arxiv.org/pdf/2304.06911v1.pdf) [`pre-training`]

  - [Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding](https://arxiv.org/pdf/2304.06906v1.pdf) [`seg`, `det`; [Github](https://github.com/microsoft/Swin3D)]

  - [RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions](https://arxiv.org/pdf/2304.06719v1.pdf) [`autonomous driving`; [Github](https://github.com/Daniel-xsy/RoboBEV)]

  - [BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo](https://arxiv.org/pdf/2304.04185v1.pdf) [`det`]

  - [VPFusion: Towards Robust Vertical Representation Learning for 3D Object Detection](https://arxiv.org/pdf/2304.02867v1.pdf) [`det`]

  - [Geometric-aware Pretraining for Vision-centric 3D Object Detection](https://arxiv.org/pdf/2304.03105v1.pdf) [`det`; [PyTorch](https://github.com/OpenDriveLab/BEVPerception-Survey-Recipe)]

  - [VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection](https://arxiv.org/pdf/2304.01054v1.pdf) [`det`; [PyTorch](https://github.com/Lizhuoling/VoxelFormer-public)]

  - [EA-BEV: Edge-aware Bird' s-Eye-View Projector for 3D Object Detection](https://arxiv.org/pdf/2303.17895v1.pdf) [`det`; [Github](https://github.com/hht1996ok/EA-BEV)]

  - [IC-FPS: Instance-Centroid Faster Point Sampling Module for 3D Point-base Object Detection](https://arxiv.org/pdf/2303.17921v1.pdf) [`det`; [Github](https://github.com/hht1996ok/IC-FPS)]

  - [APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding](https://arxiv.org/pdf/2303.17815v1.pdf) [`seg`, `cls`]

  - [BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregation](https://arxiv.org/pdf/2303.17099.pdf) [`det`]

  - [ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box](https://arxiv.org/pdf/2303.15334.pdf) [`tracking`; [Github](https://github.com/ifzhang/ByteTrack-V2)]

  - [3D Data Augmentation for Driving Scenes on Camera](https://arxiv.org/pdf/2303.10340v1.pdf) [`autonomous driving`]

  - [Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction](https://arxiv.org/pdf/2303.10552v1.pdf) [`autonomous driving`; [PyTorch](https://github.com/haibao-yu/FFNet-VIC3D)]

  - [A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving](https://arxiv.org/pdf/2303.10076v1.pdf) [`autonomous driving`; [Github](https://github.com/GANWANSHUI/SimpleOccupancy)]

  - [DiffBEV: Conditional Diffusion Model for Bird's Eye View Perception](https://arxiv.org/pdf/2303.08333v1.pdf) [`seg`, `det`; [PyTorch](https://github.com/JiayuZou2020/DiffBEV)]

  - [GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue](https://arxiv.org/pdf/2303.08274v1.pdf) [`seg`]

  - [Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perception](https://arxiv.org/pdf/2303.05970v1.pdf) [`autonomous driving`]

  - [OccDepth: A Depth-Aware Method for 3D Semantic Scene Completion](https://arxiv.org/pdf/2302.13540.pdf) [`autonomous driving`; [PyTorch](https://github.com/megvii-research/OccDepth)]

  - [DA-BEV: Depth Aware BEV Transformer for 3D Object Detection](https://arxiv.org/pdf/2302.13002.pdf) [`det`]

  - [Pillar R-CNN for Point Cloud 3D Object Detection](https://arxiv.org/pdf/2302.13301.pdf) [`det`]

  - [General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment](https://arxiv.org/pdf/2302.09907.pdf) [`cls`, `seg`, `det`]

  - [AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation](https://arxiv.org/pdf/2302.00885.pdf) [`det`, `seg`]

  - [On the Adversarial Robustness of Camera-based 3D Object Detection](https://arxiv.org/pdf/2301.10766.pdf) [`det`]

  - [OA-BEV: Bringing Object Awareness to Bird’s-Eye-View Representation for Multi-Camera 3D Object Detection](https://arxiv.org/pdf/2301.05711v1.pdf) [`det`]

  - [SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation](https://arxiv.org/pdf/2301.06869v1.pdf) [`seg`]

  - [FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels](https://arxiv.org/pdf/2308.03755.pdf) [`det`; [PyTorch](https://github.com/tusen-ai/SST)]

  - [Overlap Bias Matching is Necessary for Point Cloud Registration](https://arxiv.org/pdf/2308.09364.pdf) [`registration`]

  - [One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration](https://arxiv.org/pdf/2307.14019.pdf) [`registration`]

  - [Direct Superpoints Matching for Fast and Robust Point Cloud Registration](https://arxiv.org/pdf/2307.01362.pdf) [`registration`]

## 2022

- ECCV

  - [PCR-CG: Point Cloud Registration via Color and Geometry](https://arxiv.org/pdf/2302.14418v1.pdf) [`registration`; [PyTorch](https://github.com/Gardlin/PCR-CG)]

  - [SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement](https://arxiv.org/pdf/2211.08250.pdf) [`cls`, `seg`; [PyTorch](https://github.com/ZhaofanQiu/SPE-Net)]

  - [Image2Point: 3D Point-Cloud Understanding withPretrained 2D ConvNets](https://arxiv.org/pdf/2106.04180.pdf) [`cls`, `seg`; [Github](https://github.com/chenfengxu714/image2point)]

  - [Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection](https://arxiv.org/pdf/2211.07171.pdf) [`det`; [PyTorch](https://github.com/Cc-Hy/CMKD)]

  - [LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation](https://arxiv.org/pdf/2211.05997.pdf) [`seg`; [Github](https://github.com/hzykent/LiDAL)]

  - [DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection](https://arxiv.org/pdf/2203.09510v1.pdf) [`det`; [Github](https://github.com/Divadi/DetMatch)]

  - [Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection](https://arxiv.org/pdf/2210.09615.pdf) [`det`]

  - [Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining](https://arxiv.org/pdf/2210.08375.pdf) [`det`]

  - [CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection](https://arxiv.org/pdf/2210.09267.pdf) [`det`]

  - [LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds](https://arxiv.org/pdf/2210.08064.pdf) [`seg`]

  - [Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction](https://arxiv.org/pdf/2210.07424.pdf) [`det`; [PyTorch](https://github.com/wyndwarrior/autoregressive-bbox)]

  - [SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds](https://arxiv.org/pdf/2210.07372.pdf) [`det`]

  - [LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds](https://arxiv.org/pdf/2210.05018.pdf) [`NAS`]

  - [FBNet: Feedback Network for Point Cloud Completion](https://arxiv.org/pdf/2210.03974.pdf) [`completion`; [PyTorch](https://github.com/hikvision-research/3DVision/)]

  - [INT: Towards Infinite-frames 3D Detection with An Efficient Framework](https://arxiv.org/pdf/2209.15215.pdf) [`det`]

  - [MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection](https://arxiv.org/pdf/2205.05979.pdf) [`det`; [Github](https://github.com/open-mmlab/OpenPCDet)]

  - [PointInst3D: Segmenting 3D Instances by Points](https://arxiv.org/pdf/2204.11402.pdf) [`seg`; [Github](https://github.com/tonghe90/PointInst3D)]

  - [PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection](https://arxiv.org/pdf/2205.07403.pdf) [`det`; [PyTorch](https://github.com/agent-sgs/PillarNet)]

  - [GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation](https://arxiv.org/pdf/2204.07733.pdf) [`autonomous driving`]

  - [Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter](https://arxiv.org/pdf/2207.10859.pdf) [`seg`; [Github](https://github.com/VinAIResearch/GeoFormer)]

  - [Multimodal Transformer for Automatic 3D Annotation and Object Detection](https://arxiv.org/pdf/2207.09805.pdf) [`det`; [PyTorch](https://github.com/Cliu2/MTrans)]

  - [Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes](https://arxiv.org/pdf/2206.01203.pdf) [`seg`; [PyTorch](https://github.com/jchibane/Box2Mask)]

  - [Online Segmentation of LiDAR Sequences: Dataset and Algorithm](https://arxiv.org/pdf/2206.08194.pdf) [`seg`; [PyTorch](https://github.com/romainloiseau/Helix4D)]

  - [SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse views](https://arxiv.org/pdf/2206.05737.pdf) [`reconstruction`; [PyTorch](https://github.com/xxlong0/SparseNeuS)]

  - [Language-Grounded Indoor 3D Semantic Segmentation in the Wild](https://arxiv.org/pdf/2204.07761.pdf) [`seg`; [PyTorch](https://github.com/RozDavid/LanguageGroundedSemseg)]

  - [CenterFormer: Center-based Transformer for 3D Object Detection](https://arxiv.org/pdf/2209.05588.pdf) [`det`; [Github](https://github.com/TuSimple/centerformer)]

  - [Learning to Generate Realistic LiDAR Point Clouds](https://arxiv.org/pdf/2209.03954.pdf) [`generation`; [Project](https://www.zyrianov.org/lidargen/)]

  - [PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration](https://arxiv.org/pdf/2209.00219.pdf) [`registration`; [Github](https://github.com/phdymz/PointCLM)]

  - [SimpleRecon: 3D Reconstruction Without 3D Convolutions](https://arxiv.org/pdf/2208.14743.pdf) [`reconstruction`; [Project](https://nianticlabs.github.io/simplerecon/)]

  - [Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation](https://arxiv.org/pdf/2208.14893.pdf) [`registration`; [Github](https://github.com/514DNA/LLT)]

  - [Masked Discrimination for Self-Supervised Learning on Point Clouds](https://arxiv.org/pdf/2203.11183v1.pdf) [`self-supervised`; [PyTorch](https://github.com/haotian-liu/MaskPoint)]

  - [MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes](https://arxiv.org/abs/2203.05203) [`dense captioning`; [Github](https://github.com/SxJyJay/MORE)]

  - [Objects Can Move: 3D Change Detection by Geometric Transformation Constistency](https://arxiv.org/pdf/2208.09870.pdf) [`change detection`; [Github](https://github.com/katadam/ObjectsCanMove)]

  - [PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees](https://arxiv.org/pdf/2208.05962.pdf) [`cls`, `seg`; [PyTorch](https://github.com/immortalCO/PointTree)]

  - [diffConv: Analyzing Irregular Point Clouds with an Irregular View](https://arxiv.org/pdf/2111.14658.pdf) [`cls`, `seg`; [PyTorch](https://github.com/mmmmimic/diffConvNet)]

  - [SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty](https://arxiv.org/pdf/2208.04043.pdf) [`de-snowing`]

  - [Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph](https://arxiv.org/pdf/2208.03624.pdf) [`det`; [PyTorch](https://github.com/Nightmare-n/GraphRCNN)]

  - [Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding](https://arxiv.org/pdf/2208.00281.pdf) [`4D`]

  - [Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network](https://arxiv.org/pdf/2208.00183.pdf) [`reconstruction`]

  - [NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors](https://arxiv.org/pdf/2206.13597.pdf) [`reconstruction`; [Project](https://jiepengwang.github.io/NeuRIS/)]

  - [PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking?](https://arxiv.org/pdf/2208.01957.pdf) [`tracking`]

  - [SpOT: Spatiotemporal Modeling for 3D Object Tracking](https://arxiv.org/pdf/2207.05856.pdf) [`tracking`; [PyTorch](https://github.com/coltonstearns/SpOT)]

  - [MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud](https://arxiv.org/pdf/2207.14268.pdf) [`representation`]

  - [Large-displacement 3D Object Tracking with Hybrid Non-local Optimization](https://arxiv.org/pdf/2207.12620.pdf) [`tracking`; [Github](https://github.com/cvbubbles/nonlocal-3dtracking)]

  - [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://arxiv.org/pdf/2111.09621.pdf) [`track`; [Github](https://github.com/TuSimple/SimpleTrack)]

  - [ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection](https://arxiv.org/pdf/2207.12654.pdf) [`det`; [Github](https://github.com/yinjunbo/ProposalContrast)]

  - [Semi-supervised 3D Object Detection with Proficient Teachers](https://arxiv.org/pdf/2207.12655.pdf) [`det`; [Github](https://github.com/yinjunbo/ProficientTeachers)]

  - [Monocular 3D Object Detection with Depth from Motion](https://arxiv.org/pdf/2207.12988.pdf) [`det`; [PyTorch](https://github.com/Tai-Wang/Depth-from-Motion)]

  - [GraphFit: Learning Multi-scale Graph-Convolutional Representation for Point Cloud Normal Estimation](https://arxiv.org/pdf/2207.11484.pdf) [`normal estimation`; [PyTorch](https://github.com/UestcJay/GraphFit)]

  - [PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation](https://arxiv.org/pdf/2207.11790.pdf) [`completion`; [PyTorch](https://github.com/GitBoSun/PatchRD)]

  - [Label-Guided Auxiliary Training Improves 3D Object Detector](https://arxiv.org/pdf/2207.11753.pdf) [`det`; [Github](https://github.com/FabienCode/LG3D)]

  - [Salient Object Detection for Point Clouds](https://arxiv.org/pdf/2207.11889.pdf) [`det`; [Code](https://git.openi.org.cn/OpenPointCloud/PCSOD)]

  - [3D Siamese Transformer Network for Single Object Tracking on Point Clouds](https://arxiv.org/pdf/2207.11995.pdf) [`tracking`; [Github](https://github.com/fpthink/STNet)]

  - [Point Cloud Compression with Sibling Context and Surface Priors](https://arxiv.org/pdf/2205.00760.pdf) [`compression`; [PyTorch](https://github.com/zlichen/PCC-S)]

  - [SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds with 1000× Fewer Labels](https://arxiv.org/pdf/2104.04891.pdf) [`seg`; [Tensorflow](https://github.com/QingyongHu/SQN)]

  - [PointMixer: MLP-Mixer for Point Cloud Understanding](https://arxiv.org/pdf/2111.11187.pdf) [`seg`, `cls`, `reconstruction`; [PyTorch](https://github.com/LifeBeyondExpectations/ECCV22-PointMixer)]

  - [DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection](https://arxiv.org/pdf/2207.10758.pdf) [`det`, `monocular`; [PyTorch](https://github.com/abhi1kumar/DEVIANT)]

  - [Dynamic 3D Scene Analysis by Point Cloud Accumulation](https://arxiv.org/pdf/2207.12394.pdf) [`accumulation`; [PyTorch](https://github.com/prs-eth/PCAccumulation)]

  - [MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis](https://arxiv.org/pdf/2207.10228.pdf) [`self-supervised`]

  - [SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformer](https://arxiv.org/pdf/2207.10315.pdf) [`completion`; [PyTorch](https://github.com/hrzhou2/seedformer)]

  - [Unsupervised Deep Multi-Shape Matching](https://arxiv.org/pdf/2207.09610.pdf) [`matching`]

  - [Monocular 3D Object Reconstruction with GAN Inversion](https://arxiv.org/pdf/2207.10061.pdf) [`reconstruction`; [PyTorch](https://github.com/junzhezhang/mesh-inversion)]

  - [CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation](https://arxiv.org/pdf/2207.09778.pdf) [`seg`; [PyTorch](https://github.com/saltoricristiano/cosmix-uda)]

  - [GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation](https://arxiv.org/pdf/2207.09763.pdf) [`seg`; [PyTorch](https://github.com/saltoricristiano/gipso-sfouda)]

  - [Densely Constrained Depth Estimator for Monocular 3D Object Detection](https://arxiv.org/pdf/2207.10047.pdf) [`det`, `monocular`; [PyTorch](https://github.com/BraveGroup/DCD)]

  - [SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud](https://arxiv.org/pdf/2208.01925.pdf) [`registration`; [Tensorflow](https://github.com/zxrzju/SuperLine3D)]

  - [What Matters for 3D Scene Flow Network](https://arxiv.org/pdf/2207.09143.pdf) [`scene flow`; [PyTorch](https://github.com/IRMVLab/3DFlow)]

  - [Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation](https://arxiv.org/pdf/2207.09084.pdf) [`seg`]

  - [Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes](https://arxiv.org/pdf/2207.08656.pdf) [`reconstruction`; [Github](https://github.com/UncleMEDM/InstPIFu)]

  - [JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes](https://arxiv.org/pdf/2207.07895.pdf) [`autonomous driving`; [PyTorch](https://github.com/sunnyHelen/JPerceiver)]

  - [TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance](https://arxiv.org/pdf/2207.07861.pdf) [`pose estimation`; [PyTorch](https://github.com/yanjh97/TransGrasp)]

  - [CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement](https://arxiv.org/pdf/2207.08082.pdf) [`pose estimation`; [PyTorch](https://github.com/THU-DA-6D-Pose-Group/CATRE)]

  - [Lidar Point Cloud Guided Monocular 3D Object Detection](https://arxiv.org/pdf/2104.09035.pdf) [`det`, `monocular`; [PyTorch](https://github.com/SPengLiang/LPCG)]

  - [DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection](https://arxiv.org/pdf/2207.08531.pdf) [`det`, `monocular`; [PyTorch](https://github.com/SPengLiang/DID-M3D)]

  - [ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning](https://arxiv.org/pdf/2207.07601.pdf) [`autonomous driving`; [PyTorch](https://github.com/OpenPerceptionX/ST-P3)]

  - [3D Instances as 1D Kernels](https://arxiv.org/pdf/2207.07372.pdf) [`seg`; [PyTorch](https://github.com/W1zheng/DKNet)]

  - [Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation](https://arxiv.org/pdf/2207.07522.pdf) [`scene flow`; [PyTorch](https://github.com/cwc1260/BiFlow)]

  - [Rethinking IoU-based Optimization for Single-stage 3D Object Detection](https://arxiv.org/pdf/2207.09332.pdf) [`det`; [Github](https://github.com/hlsheng1/RDIoU)]

  - [Meta-Sampler: Almost-Universal yet Task-Oriented Sampling for Point Clouds](https://arxiv.org/pdf/2203.16001.pdf) [`sampling`]

  - [CPO: Change Robust Panorama to Point Cloud Localization](https://arxiv.org/pdf/2207.05317.pdf) [`visual localization`]

  - [Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks](https://arxiv.org/pdf/2207.05444.pdf) [`pose estimation`; [PyTorch](https://github.com/JiehongLin/Self-DPDN)]

  - [Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting](https://arxiv.org/pdf/2207.04624.pdf) [`autonomous driving`; [PyTorch](https://github.com/d1024choi/HLSTrajForecast)]

  - [2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds](https://arxiv.org/pdf/2207.04397.pdf) [`seg`; [Github](https://github.com/yanx27/2DPASS)]

  - [A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision](https://arxiv.org/pdf/2207.04997.pdf) [`self-supervised`]

  - [Open-world Semantic Segmentation for LIDAR Point Clouds](https://arxiv.org/pdf/2207.01452.pdf) [`seg`; [PyTorch](https://github.com/Jun-CEN/Open_world_3D_semantic_segmentation)]

  - [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://arxiv.org/pdf/2203.17270.pdf) [`det`, `seg`; [Github](https://github.com/zhiqi-li/BEVFormer)]

  - [LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection](https://arxiv.org/pdf/2203.14956.pdf) [`det`; [PyTorch](https://github.com/weiyithu/LiDAR-Distillation)]

  - [FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection](https://arxiv.org/pdf/2112.00322v1.pdf) [`det`; [PyTorch](https://github.com/samsunglabs/fcaf3d)]

  - [Masked Autoencoders for Point Cloud Self-supervised Learning](https://arxiv.org/pdf/2203.06604.pdf) [`self-supervised`; [PyTorch](https://github.com/Pang-Yatian/Point-MAE)]

  - [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://arxiv.org/pdf/2203.05625.pdf) [`det`; [PyTorch](https://github.com/megvii-research/PETR)]

  - [Learning Ego 3D Representation as Ray Tracing](https://arxiv.org/pdf/2206.04042.pdf) [`autonomous driving`; [PyTorch](https://github.com/fudan-zvg/Ego3RT)]

  - [AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection](https://arxiv.org/pdf/2207.10316.pdf) [`det`; [Github](https://github.com/zehuichen123/AutoAlignV2)]

- CVPR

  - [Bridged Transformer for Vision and Point Cloud 3D Object Detection](https://arxiv.org/pdf/2210.01391.pdf) [`det`]

  - [ShapeFormer: Transformer-based Shape Completion via Sparse Representation](https://arxiv.org/pdf/2201.10326.pdf) [`completion`; [PyTorch](https://github.com/qheldiv/shapeformer)]

  - [RBGNet: Ray-based Grouping for 3D Object Detection](https://arxiv.org/pdf/2204.02251.pdf) [`det`; [Github](https://github.com/Haiyang-W/RBGNet)]

  - [Boosting 3D Object Detection by Simulating Multimodality on Point Clouds](https://arxiv.org/pdf/2206.14971.pdf) [`det`]

  - [MonoGround: Detecting Monocular 3D Objects from the Ground](https://arxiv.org/pdf/2206.07372.pdf) [`det`, `monocular`; [Github](https://github.com/cfzd/MonoGround)]

  - [PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos](https://arxiv.org/pdf/2206.07710.pdf) [`reconstruction`; [PyTorch](https://github.com/neu-vi/planarrecon)]

  - [Learning 3D Object Shape and Layout without 3D Supervision](https://arxiv.org/pdf/2206.07028.pdf) [`shape`, `layout`; [Project](https://gkioxari.github.io/usl/)]

  - [Deterministic Point Cloud Registration via Novel Transformation Decomposition](https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.pdf) [`registration`]

  - [Cross-view Transformers for real-time Map-view Semantic Segmentation](https://arxiv.org/pdf/2205.02833.pdf) [`autonomous driving`; [PyTorch](https://github.com/bradyz/cross_view_transformers)]

  - [RIDDLE: Lidar Data Compression with Range Image Deep Delta Encoding](https://arxiv.org/pdf/2206.01738.pdf) [`compression`]

  - [CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance](https://arxiv.org/pdf/2203.09887v1.pdf) [`seg`; [PyTorch](https://github.com/A-suozhang/CodedVTR)]

  - [Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation](https://arxiv.org/pdf/2206.02099.pdf) [`seg`; [Github](https://github.com/cardwing/Codes-for-PVKD)]

  - [Voxel Field Fusion for 3D Object Detection](https://arxiv.org/pdf/2205.15938.pdf) [`det`; [PyTorch](https://github.com/dvlab-research/VFF)]

  - [On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models](https://arxiv.org/pdf/2206.00608.pdf) [`autonomous driving`; CVPRW]

  - [3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologies](https://arxiv.org/pdf/2205.15572.pdf) [`reconstruction`]

  - [The Devil is in the Pose: Ambiguity-free 3D Rotation-invariant Learning via Pose-aware Convolution](https://arxiv.org/pdf/2205.15210.pdf) [`cls`, `seg`]

  - [Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors](https://arxiv.org/pdf/2205.14886.pdf) [`shape mating`; [Project](https://neural-shape-mating.github.io)]

  - [Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving](https://arxiv.org/pdf/2205.14882.pdf) [`autonomous driving`, `monocular`]

  - [SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation](https://arxiv.org/pdf/2205.13490.pdf) [`seg`; [PyTorch](https://github.com/wangzy22/SemAffiNet)]

  - [Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives](https://arxiv.org/pdf/2205.12627.pdf) [`cls`]

  - [RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds](https://arxiv.org/pdf/2205.11028.pdf) [`scene flow`; [Github](https://github.com/gxd1994/RCP)]

  - [Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection](https://arxiv.org/pdf/2205.09373.pdf) [`det`]

  - [Surface Representation for Point Clouds](https://arxiv.org/pdf/2205.05740.pdf) [`cls`, `seg`, `det`; [PyTorch](https://github.com/hancyran/RepSurf)]

  - [FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction](https://arxiv.org/pdf/2205.07763.pdf) [`reconstruction`; [PyTorch](https://github.com/zhenpeiyang/FvOR/)]

  - [Topologically-Aware Deformation Fields for Single-View 3D Reconstruction](https://arxiv.org/pdf/2205.06267.pdf) [`reconstruction`; [Project](https://shivamduggal4.github.io/tars-3D/)]

  - [Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching](https://arxiv.org/pdf/2204.13453.pdf) [`matching`; [PyTorch](https://github.com/nicolasdonati/DUO-FM)]

  - [Rotationally Equivariant 3D Object Detection](https://arxiv.org/pdf/2204.13630.pdf) [`det`; [Project](https://kovenyu.com/eon/)]

  - [MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation](https://arxiv.org/pdf/2204.12667.pdf) [`seg`; [Project](https://www.nec-labs.com/~mas/MM-TTA/)]

  - [Density-preserving Deep Point Cloud Compression](https://arxiv.org/pdf/2204.12684.pdf) [`compression`; [PyTorch](https://github.com/yunhe20/D-PCC)]

  - [Coupled Iterative Refinement for 6D Multi-Object Pose Estimation](https://arxiv.org/pdf/2204.12516.pdf) [`pose estimation`; [PyTorch](https://github.com/princeton-vl/Coupled-Iterative-Refinement)]

  - [A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching](https://arxiv.org/pdf/2204.12805.pdf) [`match`; [Github](https://github.com/paul0noah/sm-comb)]

  - [Focal Sparse Convolutional Networks for 3D Object Detection](https://arxiv.org/pdf/2204.12463.pdf) [`det`; [PyTorch](https://github.com/dvlab-research/FocalsConv)]

  - [Surface Reconstruction from Point Clouds by Learning Predictive Context Priors](https://arxiv.org/pdf/2204.11015.pdf) [`reconstruction`; [Tensorflow](https://github.com/mabaorui/PredictableContextPrior)]

  - [Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors](https://arxiv.org/pdf/2204.10603.pdf) [`reconstruction`; [Tensorflow](https://github.com/mabaorui/OnSurfacePrior)]

  - [Forecasting from LiDAR via Future Object Detection](https://arxiv.org/pdf/2203.16297.pdf) [`forecasting`; [PyTorch](https://github.com/neeharperi/FutureDet)]

  - [Fast Point Transformer](https://arxiv.org/pdf/2112.04702.pdf) [`seg`, `det`; [PyTorch](https://github.com/POSTECH-CVLab/FastPointTransformer)]

  - [Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity](https://arxiv.org/pdf/2204.08744.pdf) [`seg`; CVPRW]

  - [Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles](https://arxiv.org/pdf/2204.09151.pdf) [`autonomous driving`; CVPRW]

  - [Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation](https://arxiv.org/pdf/2204.07548.pdf) [`seg`; [PyTorch](https://github.com/drprojects/DeepViewAgg)]

  - [OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data](https://arxiv.org/pdf/2204.06577.pdf) [`det`; [PyTorch](https://github.com/dschinagl/occam)]

  - [3DeformRS: Certifying Spatial Deformations on Point Clouds](https://arxiv.org/pdf/2204.05687.pdf) [`robustness`; [Github](https://github.com/gaperezsa/3DeformRS)]

  - [HyperDet3D: Learning a Scene-conditioned 3D Object Detector](https://arxiv.org/pdf/2204.05599.pdf) [`det`]

  - [Exploiting Temporal Relations on Radar Perception for Autonomous Driving](https://arxiv.org/pdf/2204.01184.pdf) [`autonomous driving`]

  - [Homography Loss for Monocular 3D Object Detection](https://arxiv.org/pdf/2204.00754.pdf) [`det`]

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

  - [Learning to Detect Mobile Objects from LiDAR Scans Without Labels](https://arxiv.org/pdf/2203.15882.pdf) [`det`; [PyTorch](https://github.com/YurongYou/MODEST)]

  - [Learning Local Displacements for Point Cloud Completion](https://arxiv.org/pdf/2203.16600.pdf) [`completion`]

  - [Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds](https://arxiv.org/pdf/2203.16895.pdf) [`scene flow`; [Github](https://github.com/leolyj/DCA-SRSFE)]

  - [LiDAR Snowfall Simulation for Robust 3D Object Detection](https://arxiv.org/pdf/2203.15118.pdf) [`det`; [Github](https://github.com/SysCV/LiDAR_snow_sim)]

  - [Text2Pos: Text-to-Point-Cloud Cross-Modal Localization](https://arxiv.org/pdf/2203.15125.pdf) [`localization`; [PyTorch](https://github.com/mako443/Text2Pos-CVPR2022)]

  - [Stratified Transformer for 3D Point Cloud Segmentation](https://arxiv.org/pdf/2203.14508.pdf) [`seg`; [PyTorch](https://github.com/dvlab-research/Stratified-Transformer)]

  - [REGTR: End-to-end Point Cloud Correspondences with Transformers](https://arxiv.org/pdf/2203.14517.pdf) [`registration`; [PyTorch](https://github.com/yewzijian/RegTR)]

  - [Equivariant Point Cloud Analysis via Learning Orientations for Message Passing](https://arxiv.org/pdf/2203.14486.pdf) [`cls`, `seg`, `normal estimation`; [Github](https://github.com/luost26/Equivariant-OrientedMP)]

  - [SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration](https://arxiv.org/pdf/2203.14453.pdf) [`registration`; [Github](https://github.com/ZhiChen902/SC2-PCR)]

  - [Multi-instance Point Cloud Registration by Efficient Correspondence Clustering](https://arxiv.org/pdf/2111.14582.pdf) [`registration`]

  - [Towards Implicit Text-Guided 3D Shape Generation](https://arxiv.org/pdf/2203.14622.pdf) [`generation`; [PyTorch](https://github.com/liuzhengzhe/Towards-Implicit-Text-Guided-Shape-Generation)]

  - [Point2Seq: Detecting 3D Objects as Sequences](https://arxiv.org/pdf/2203.13394v1.pdf) [`det`; [PyTorch](https://github.com/ocNflag/point2seq)]

  - [MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection](https://arxiv.org/pdf/2203.13310v1.pdf) [`det`, `monocular`; [Github](https://github.com/ZrrSkywalker/MonoDETR)]

  - [AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception](https://arxiv.org/pdf/2203.13090v1.pdf) [`det`, `seg`; [Github](https://github.com/hustvl/AziNorm)]

  - [IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment](https://arxiv.org/pdf/2203.11590v1.pdf) [`interpolation`; [Github](https://github.com/ZENGYIMING-EAMON/IDEA-Net)]

  - [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://arxiv.org/pdf/2203.11496v1.pdf) [`det`; [PyTorch](https://github.com/XuyangBai/TransFusion/)]

  - [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://arxiv.org/pdf/2203.11139v1.pdf) [`det`; [PyTorch](https://github.com/yifanzhang713/IA-SSD)]

  - [No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces](https://arxiv.org/pdf/2203.11113v1.pdf) [`cls`; [Github](https://github.com/jx-zhong-for-academic-purpose/Kinet)]

  - [MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer](https://arxiv.org/pdf/2203.10981v1.pdf) [`det`, `monocular`; [Github](https://github.com/kuanchihhuang/MonoDTR)]

  - [Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds](https://arxiv.org/pdf/2203.10314v1.pdf) [`det`; [PyTorch](https://github.com/skyhehe123/VoxSeT)]

  - [VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention](https://arxiv.org/pdf/2203.09704v1.pdf) [`det`; [PyTorch](https://github.com/Gorilla-Lab-SCUT/VISTA)]

  - [Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion](https://arxiv.org/pdf/2203.09780v1.pdf) [`det`]

  - [AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation](https://arxiv.org/pdf/2203.09516v1.pdf) [`completion`, `reconstruction`, `generation`]

  - [DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection](https://arxiv.org/pdf/2203.08195v1.pdf) [`det`; [Tensorflow](https://github.com/tensorflow/lingvo/tree/master/lingvo)]

  - [Scribble-Supervised LiDAR Semantic Segmentation](https://arxiv.org/pdf/2203.08537v1.pdf) [`seg`; [Github](https://github.com/ouenal/scribblekitti)]

  - [MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection](https://arxiv.org/pdf/2203.08563v1.pdf) [`det`, `monocular`; [Github](https://github.com/lianqing11/MonoJSG)]

  - [PTTR: Relational 3D Point Cloud Object Tracking with Transformer](https://arxiv.org/pdf/2112.02857.pdf) [`tracking`; [PyTorch](https://github.com/Jasonkks/PTTR)]

  - [AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation](https://arxiv.org/pdf/2203.06558.pdf) [`seg`]

  - [Point Density-Aware Voxels for LiDAR 3D Object Detection](https://arxiv.org/pdf/220