Awesome paper/code for point clouds with deep learning methods in detection and tracking. If you find some novel methods or have suggestions, please contact ybcui95@163.com
- KITTI: Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite [Project Page] [CVPR'2012]
- Apolloscape: The ApolloScape Dataset for Autonomous Driving [Project Page] [CVPR'2018]
- Argoverse: Argoverse: 3D Tracking and Forecasting with Rich Maps [Project Page] [CVPR'2019]
- Nuscenes: nuScenes: A multimodal dataset for autonomous driving [Project Page] [arXiv'2019]
- H3D: The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes [Project Page] [ICRA'2019]
- BLVD: BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving [Project Page] [ICRA'2019]
- Waymo: Scalability in Perception for Autonomous Driving: Waymo Open Dataset [Project Page] [arXiv'2019]
- A* 3D: A* 3D: An Autonomous Driving Dataset in Challeging Environments [Project Page] [ICRA'2020]
- Ford AV Dataset : Ford Multi-AV Seasonal Dataset [Project Page] [arXiv'2020]
- A2D2 : A2D2: Audi Autonomous Driving Dataset [Project Page] [arXiv'2020]
- ONCE : One Million Scenes for Autonomous Driving: ONCE Dataset [Project Page] [NeurIPS'2021]
- Argoverse 2 : Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting [Project Page] [NeurIPS'2021]
- DAIR-V2X : DAIR-V2X : A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection [Project Page] [CVPR'2022]
- MMDetection3D: MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection.
- OpenPCDet: OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection.
- MV3D: Multi-View 3D Object Detection Network for Autonomous Driving [Code] [CVPR'2017]
- Frustum-Pointnets: Frustum PointNets for 3D Object Detection from RGB-D Data [Code] [CVPR'2018]
- PIXOR: PIXOR: Real-time 3D Object Detection from Point Clouds [Code] [CVPR'2018]
- IPOD: IPOD: Intensive Point-based Object Detector for Point Cloud [arXiv'2018]
- VoxelNet: VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [Code] [CVPR'2018]
- FaF: Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [CVPR'2018]
- Second: SECOND: Sparsely Embedded Convolutional Detection [Code] [Sensors'2018]
- AVOD: Joint 3D Proposal Generation and Object Detection from View Aggregation [Code] [IROS'2018]
- RoarNet: RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement [Code] [IV'2019]
- PointPillars: PointPillars: Fast Encoders for Object Detection from Point Clouds [Code] [CVPR'2019]
- PointRCNN: PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [Code] [CVPR'2019]
- Pseudo-LiDAR: Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving [Code] [CVPR'2019]
- LaserNet: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [CVPR'2019]
- LaserNet++: Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation [CVPR'2019 Workshop]
- Fast Point R-CNN: Fast Point R-CNN [ICCV'2019]
- STD: STD: Sparse-to-Dense 3D Object Detector for Point Cloud [Code] [ICCV'2019]
- PointPainting: PointPainting: Sequential Fusion for 3D Object Detection[arXiv'2019]
- Part-A^2: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network [Code] [TPAMI'2020]
- Pseudo-LiDAR++: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving [Code] [ICLR'2020]
- PV-RCNN: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection [Code] [CVPR'2020]
- Point-GNN: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [Code] [CVPR'2020]
- SA-SSD: Structure Aware Single-stage 3D Object Detection from Point Cloud [Code] [CVPR'2020]
- 3DSSD: 3DSSD: Point-based 3D Single Stage Object Detector [Code] [CVPR'2020]
- EPNet: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection [Code] [ECCV'2020]
- HotSpot: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots [ECCV'2020]
- Pillar-MVF: Pillar-based Object Detection for Autonomous Driving [ECCV'2020]
- Pyramid R-CNN: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection [Code] [ICCV'2021]
- VoTr: Voxel Transformer for 3D Object Detection [Code] [ICCV'2021]
- 4D-Net: 4D-Net for Learned Multi-Modal Alignment [ICCV'2021]
- RangeDet: RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection [Code] [ICCV'2021]
- LiDAR R-CNN: LiDAR R-CNN: An Efficient and Universal 3D Object Detector [CVPR'2021]
- MVP: Multimodal Virtual Point 3D Detection [Code] [NeurIPS'2021]
- RSN: RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection [CVPR'2021]
- PDV: Point Density-Aware Voxels for LiDAR 3D Object Detection [Code] [CVPR'2022]
- VoxSeT: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds [Code] [CVPR'2022]
- Point2Seq: Point2Seq: Detecting 3D Objects as Sequences [Code] [CVPR'2022]
- PillarNet: PillarNet : Real-Time and High-Performance Pillar-based 3D Object Detection [Code] [ECCV'2022]
- VoteNet: Deep Hough Voting for 3D Object Detection in Point Clouds [Code] [ICCV'2019]
- ImVoteNet: ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes [Code] [CVPR'2020]
- 3DETR: An End-to-End Transformer Model for 3D Object Detection [Code] [ICCV'2021]
- VENet: VENet: Voting Enhancement Network for 3D Object Detection [ICCV'2021]
- Group-Free: Group-Free 3D Object Detection via Transformers [Code] [ICCV'2021]
- RBGNet: RBGNet: Ray-based Grouping for 3D Object Detection [Code] [CVPR'2022]
- FCAF3D: FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection [Code] [ECCV'2022]
- Complexer-YOLO: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds [Code] [CVPR'2019 Workshop]
- 3DSiamese: Leveraging Shape Completion for 3D Siamese Tracking [Code] [CVPR'2019]
- AB3DMOT: A Baseline for 3D Multi-Object Tracking [Code] [arXiv'2019]
- mmMOT: Robust Multi-Modality Multi-Object Tracking [Code] [ICCV'2019]
- DSM: End-to-end Learning of Multi-sensor 3D Tracking by Detection [ICRA'2019]
- PointTrackNet: PointTrackNet: An End-to-End Network for 3-D Object Detection and Tracking from Point Clouds [ICRA'2020]
- Mahalanobis-KF: Probabilistic 3D Multi-Object Tracking for Autonomous Driving [Code] [arXiv'2020]
- GNN3DMOT: GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning [Code] [CVPR'2020]
- PC-TCNN: Tracklet Proposal Network for Multi-Object Tracking on Point Clouds [IJCAI'2021]
- LOGR: Learnable Online Graph Representations for 3D Multi-Object Tracking [arXiv'2021]
- CenterPoint: Center-based 3D Object Detection and Tracking [Code] [CVPR'2021]
- Immortal-Tracker: Immortal Tracker: Tracklet Never Dies [Code] [arXiv'2021]
- SimpleTrack: SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking [Code] [arXiv'2021]
- P2B: P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds [Code] [CVPR'2020]
- PTT: PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds [Code] [IROS'2021]
- BAT: Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds [Code] [ICCV'2021]
- LTTR: 3D Object Tracking with Transformer [Code] [BMVC'2021]
- V2B: 3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds [Code] [NeurIPS'2021]
- PTTR: PTTR: Relational 3D Point Cloud Object Tracking with Transformer [Code] [CVPR'2022]
- M2-Tracker: Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds [Code] [CVPR'2022]
- STNet: 3D Siamese Transformer Network for Single Object Tracking on Point Clouds [Code] [ECCV'2022]
- SMAT: Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking [Code] [RAL]