└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-Point-Clouds 2 | 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 3 | 4 | ## Datasets 5 | - KITTI: [Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite](http://www.cvlibs.net/publications/Geiger2012CVPR.pdf) [[Project Page](http://www.cvlibs.net/datasets/kitti/index.php)] [CVPR'2012] 6 | - Apolloscape: [The ApolloScape Dataset for Autonomous Driving](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w14/Huang_The_ApolloScape_Dataset_CVPR_2018_paper.pdf) [[Project Page](http://apolloscape.auto/)] [CVPR'2018] 7 | - Argoverse: [Argoverse: 3D Tracking and Forecasting with Rich Maps](http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf) [[Project Page](https://www.argoverse.org/index.html)] [CVPR'2019] 8 | - Nuscenes: [nuScenes: A multimodal dataset for autonomous driving](https://arxiv.org/pdf/1903.11027.pdf) [[Project Page](https://www.nuscenes.org/)] [arXiv'2019] 9 | - H3D: [The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes](https://arxiv.org/pdf/1903.01568.pdf) [[Project Page](https://usa.honda-ri.com//H3D)] [ICRA'2019] 10 | - BLVD: [BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving](https://arxiv.org/pdf/1903.06405v1.pdf) [[Project Page](https://github.com/VCCIV/BLVD)] [ICRA'2019] 11 | - Waymo: [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://arxiv.org/pdf/1912.04838.pdf) [[Project Page](https://waymo.com/open/)] [arXiv'2019] 12 | - A* 3D: [A* 3D: An Autonomous Driving Dataset in Challeging Environments](https://arxiv.org/pdf/1909.07541.pdf) [[Project Page](https://github.com/I2RDL2/ASTAR-3D)] [ICRA'2020] 13 | - Ford AV Dataset : [Ford Multi-AV Seasonal Dataset](https://s23.q4cdn.com/258866874/files/doc_downloads/2020/03/2003.07969.pdf) [[Project Page](https://avdata.ford.com/home/default.aspx)] [arXiv'2020] 14 | - A2D2 : [A2D2: Audi Autonomous Driving Dataset](https://arxiv.org/pdf/2004.06320.pdf) [[Project Page](https://www.a2d2.audi/a2d2/en.html)] [arXiv'2020] 15 | - ONCE : [One Million Scenes for Autonomous Driving: ONCE Dataset](https://arxiv.org/pdf/2106.11037.pdf) [[Project Page](https://once-for-auto-driving.github.io/index.html)] [NeurIPS'2021] 16 | - Argoverse 2 : [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://openreview.net/pdf?id=vKQGe36av4k) [[Project Page](https://www.argoverse.org/av2.html)] [NeurIPS'2021] 17 | - DAIR-V2X : [DAIR-V2X : A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_DAIR-V2X_A_Large-Scale_Dataset_for_Vehicle-Infrastructure_Cooperative_3D_Object_Detection_CVPR_2022_paper.pdf) [[Project Page](https://thudair.baai.ac.cn/index)] [CVPR'2022] 18 | 19 | ## Detection 20 | ### Toolbox 21 | - [MMDetection3D](https://github.com/bostondiditeam/MV3D): MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. 22 | - [OpenPCDet](https://github.com/open-mmlab/OpenPCDet): OpenPCDet is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. 23 | ### Outdoors 24 | - MV3D: [Multi-View 3D Object Detection Network for Autonomous Driving](http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Multi-View_3D_Object_CVPR_2017_paper.pdf) [[Code](https://github.com/bostondiditeam/MV3D)] [CVPR'2017] 25 | - Frustum-Pointnets: [Frustum PointNets for 3D Object Detection from RGB-D Data](http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Frustum_PointNets_for_CVPR_2018_paper.pdf) [[Code](https://github.com/charlesq34/frustum-pointnets)] [CVPR'2018] 26 | - PIXOR: [PIXOR: Real-time 3D Object Detection from Point Clouds](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3D_CVPR_2018_paper.pdf) [[Code](https://github.com/philip-huang/PIXOR)] [CVPR'2018] 27 | - IPOD: [IPOD: Intensive Point-based Object Detector for Point Cloud](https://arxiv.org/pdf/1812.05276.pdf) [arXiv'2018] 28 | - VoxelNet: [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_VoxelNet_End-to-End_Learning_CVPR_2018_paper.pdf) [[Code](https://github.com/qianguih/voxelnet)] [CVPR'2018] 29 | - FaF: [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion 30 | Forecasting with a Single Convolutional Net](http://openaccess.thecvf.com/content_cvpr_2018/papers/Luo_Fast_and_Furious_CVPR_2018_paper.pdf) [CVPR'2018] 31 | - Second: [SECOND: Sparsely Embedded Convolutional Detection](https://www.mdpi.com/1424-8220/18/10/3337) [[Code](https://github.com/traveller59/second.pytorch)] [Sensors'2018] 32 | - AVOD: [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://arxiv.org/pdf/1712.02294v4.pdf) [[Code](https://github.com/kujason/avod)] [IROS'2018] 33 | - RoarNet: [RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement](https://arxiv.org/pdf/1811.03818.pdf) [[Code](https://github.com/reinforcementdriving/RoarNet)] [IV'2019] 34 | - PointPillars: [PointPillars: Fast Encoders for Object Detection from Point Clouds](http://openaccess.thecvf.com/content_CVPR_2019/papers/Lang_PointPillars_Fast_Encoders_for_Object_Detection_From_Point_Clouds_CVPR_2019_paper.pdf) [[Code](https://github.com/nutonomy/second.pytorch)] [CVPR'2019] 35 | - PointRCNN: [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](http://openaccess.thecvf.com/content_CVPR_2019/papers/Shi_PointRCNN_3D_Object_Proposal_Generation_and_Detection_From_Point_Cloud_CVPR_2019_paper.pdf) [[Code](https://github.com/sshaoshuai/PointRCNN)] [CVPR'2019] 36 | - Pseudo-LiDAR: [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Pseudo-LiDAR_From_Visual_Depth_Estimation_Bridging_the_Gap_in_3D_CVPR_2019_paper.pdf) [[Code](https://github.com/mileyan/pseudo_lidar)] [CVPR'2019] 37 | - LaserNet: [LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving](http://openaccess.thecvf.com/content_CVPR_2019/papers/Meyer_LaserNet_An_Efficient_Probabilistic_3D_Object_Detector_for_Autonomous_Driving_CVPR_2019_paper.pdf) [CVPR'2019] 38 | - LaserNet++: [Sensor Fusion for Joint 3D Object Detection and Semantic Segmentation](http://openaccess.thecvf.com/content_CVPRW_2019/papers/WAD/Meyer_Sensor_Fusion_for_Joint_3D_Object_Detection_and_Semantic_Segmentation_CVPRW_2019_paper.pdf) [CVPR'2019 Workshop] 39 | - Fast Point R-CNN: [Fast Point R-CNN](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Fast_Point_R-CNN_ICCV_2019_paper.pdf) [ICCV'2019] 40 | - STD: [STD: Sparse-to-Dense 3D Object Detector for Point Cloud](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_STD_Sparse-to-Dense_3D_Object_Detector_for_Point_Cloud_ICCV_2019_paper.pdf) [[Code](https://github.com/tomztyang/3DSSD)] [ICCV'2019] 41 | - PointPainting: [PointPainting: Sequential Fusion for 3D Object Detection](https://arxiv.org/pdf/1911.10150.pdf)[arXiv'2019] 42 | - Part-A^2: [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://arxiv.org/pdf/1907.03670v3.pdf) [[Code](https://github.com/sshaoshuai/PCDet)] [TPAMI'2020] 43 | - Pseudo-LiDAR++: [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://openreview.net/pdf?id=BJedHRVtPB) [[Code](https://github.com/mileyan/Pseudo_Lidar_V2)] [ICLR'2020] 44 | - PV-RCNN: [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://arxiv.org/pdf/1912.13192.pdf) [[Code](https://github.com/sshaoshuai/PV-RCNN)] [CVPR'2020] 45 | - Point-GNN: [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://arxiv.org/pdf/2003.01251v1.pdf) [[Code](https://github.com/WeijingShi/Point-GNN)] [CVPR'2020] 46 | - SA-SSD: [Structure Aware Single-stage 3D Object Detection from Point Cloud](https://www4.comp.polyu.edu.hk/~cslzhang/paper/SA-SSD.pdf) [[Code](https://github.com/skyhehe123/SA-SSD)] [CVPR'2020] 47 | - 3DSSD: [3DSSD: Point-based 3D Single Stage Object Detector](https://arxiv.org/pdf/2002.10187.pdf) [[Code](https://github.com/tomztyang/3DSSD)] [CVPR'2020] 48 | - EPNet: [EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection](https://arxiv.org/pdf/2007.08856.pdf) [[Code](https://github.com/happinesslz/EPNet)] [ECCV'2020] 49 | - HotSpot: [Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots](https://arxiv.org/pdf/1912.12791.pdf) [ECCV'2020] 50 | - Pillar-MVF: [Pillar-based Object Detection for Autonomous Driving](https://arxiv.org/pdf/2007.10323.pdf) [ECCV'2020] 51 | - Pyramid R-CNN: [Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection](https://arxiv.org/pdf/2109.02499.pdf) [[Code](https://github.com/PointsCoder/Pyramid-RCNN)] [ICCV'2021] 52 | - VoTr: [Voxel Transformer for 3D Object Detection](https://arxiv.org/pdf/2109.02497.pdf) [[Code](https://github.com/PointsCoder/VOTR)] [ICCV'2021] 53 | - 4D-Net: [4D-Net for Learned Multi-Modal Alignment](https://openaccess.thecvf.com/content/ICCV2021/papers/Piergiovanni_4D-Net_for_Learned_Multi-Modal_Alignment_ICCV_2021_paper.pdf) [ICCV'2021] 54 | - RangeDet: [RangeDet: In Defense of Range View for LiDAR-based 3D Object Detection](https://openaccess.thecvf.com/content/ICCV2021/papers/Fan_RangeDet_In_Defense_of_Range_View_for_LiDAR-Based_3D_Object_ICCV_2021_paper.pdf) [[Code](https://github.com/TuSimple/RangeDet)] [ICCV'2021] 55 | - LiDAR R-CNN: [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://arxiv.org/pdf/2103.15297.pdf) [CVPR'2021] 56 | - MVP: [Multimodal Virtual Point 3D Detection](https://arxiv.org/pdf/2111.06881.pdf) [[Code](https://github.com/tianweiy/MVP)] [NeurIPS'2021] 57 | - RSN: [RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection](https://arxiv.org/pdf/2106.13365.pdf) [CVPR'2021] 58 | - PDV: [Point Density-Aware Voxels for LiDAR 3D Object Detection](https://arxiv.org/pdf/2203.05662.pdf) [[Code](https://github.com/TRAILab/PDV)] [CVPR'2022] 59 | - VoxSeT: [Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds](https://www4.comp.polyu.edu.hk/~cslzhang/paper/VoxSeT_cvpr22.pdf) [[Code](https://github.com/skyhehe123/VoxSeT)] [CVPR'2022] 60 | - SST: [Embracing Single Stride 3D Object Detector with Sparse Transformer](https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Embracing_Single_Stride_3D_Object_Detector_With_Sparse_Transformer_CVPR_2022_paper.pdf) [[Code](https://github.com/TuSimple/SST)] [CVPR'2022] 61 | - TransFusion: [TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers](https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_TransFusion_Robust_LiDAR-Camera_Fusion_for_3D_Object_Detection_With_Transformers_CVPR_2022_paper.pdf) [[Code](https://github.com/xuyangbai/transfusion)] [CVPR'2022] 62 | - Point2Seq: [Point2Seq: Detecting 3D Objects as Sequences](https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_Point2Seq_Detecting_3D_Objects_As_Sequences_CVPR_2022_paper.pdf) [[Code](https://github.com/ocNflag/point2seq)] [CVPR'2022] 63 | - PillarNet: [PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection](https://arxiv.org/pdf/2205.07403.pdf) [[Code](https://github.com/agent-sgs/PillarNet)] [ECCV'2022] 64 | - CenterFormer: [CenterFormer: Center-based Transformer for 3D Object Detection](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136980487.pdf) [[Code](https://github.com/edwardzhou130/CenterFormer)] [ECCV'2022] 65 | - Pillar R-CNN: [Pillar R-CNN for Point Cloud 3D Object Detection](https://arxiv.org/pdf/2302.13301.pdf) [[Code](https://github.com/VISION-SJTU/PillarNet-LTS)] [arXiv'2023] 66 | - DSVT: [DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_DSVT_Dynamic_Sparse_Voxel_Transformer_With_Rotated_Sets_CVPR_2023_paper.pdf) [[Code](https://github.com/Haiyang-W/DSVT)] [CVPR'2023] 67 | - FlatFormer: [FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer](https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_FlatFormer_Flattened_Window_Attention_for_Efficient_Point_Cloud_Transformer_CVPR_2023_paper.pdf) [[Code](https://github.com/mit-han-lab/flatformer)] [CVPR'2023] 68 | - VoxelNeXt: [VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking](https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_VoxelNeXt_Fully_Sparse_VoxelNet_for_3D_Object_Detection_and_Tracking_CVPR_2023_paper.pdf) [[Code](https://github.com/dvlab-research/VoxelNeXt)] [CVPR'2023] 69 | - LinK: [LinK: Linear Kernel for LiDAR-based 3D Perception](https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_LinK_Linear_Kernel_for_LiDAR-Based_3D_Perception_CVPR_2023_paper.pdf) [[Code](https://github.com/MCG-NJU/LinK)] [CVPR'2023] 70 | - LargeKernel3D: [LargeKernel3D:Scaling up Kernels in 3D CNNs](https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_LargeKernel3D_Scaling_Up_Kernels_in_3D_Sparse_CNNs_CVPR_2023_paper.pdf) [[Code](https://github.com/dvlab-research/LargeKernel3D)] [CVPR'2023] 71 | - ConQueR: [ConQueR: Query Contrast Voxel-DETR for 3D Object Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_ConQueR_Query_Contrast_Voxel-DETR_for_3D_Object_Detection_CVPR_2023_paper.pdf) [[Code](https://github.com/V2AI/EFG)] [CVPR'2023] 72 | - FocalFormer3D: [FocalFormer3D: Focusing on Hard Instance for 3D Object Detection](https://openaccess.thecvf.com/content/ICCV2023/papers/Chen_FocalFormer3D_Focusing_on_Hard_Instance_for_3D_Object_Detection_ICCV_2023_paper.pdf) [[Code](https://github.com/NVlabs/FocalFormer3D)] [ICCV'2023] 73 | - Li3DeTr: [Li3DeTr: A LiDAR based 3D Detection Transformer](https://openaccess.thecvf.com/content/WACV2023/papers/Erabati_Li3DeTr_A_LiDAR_Based_3D_Detection_Transformer_WACV_2023_paper.pdf) [WACV'2023] 74 | 75 | ### Indoors 76 | - VoteNet: [Deep Hough Voting for 3D Object Detection in Point Clouds](http://openaccess.thecvf.com/content_ICCV_2019/papers/Qi_Deep_Hough_Voting_for_3D_Object_Detection_in_Point_Clouds_ICCV_2019_paper.pdf) [[Code](https://github.com/facebookresearch/votenet)] [ICCV'2019] 77 | - ImVoteNet: [ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qi_ImVoteNet_Boosting_3D_Object_Detection_in_Point_Clouds_With_Image_CVPR_2020_paper.pdf) [[Code](https://github.com/facebookresearch/imvotenet)] [CVPR'2020] 78 | - 3DETR: [An End-to-End Transformer Model for 3D Object Detection](https://arxiv.org/pdf/2109.08141.pdf) [[Code](https://facebookresearch.github.io/3detr)] [ICCV'2021] 79 | - VENet: [VENet: Voting Enhancement Network for 3D Object Detection](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_VENet_Voting_Enhancement_Network_for_3D_Object_Detection_ICCV_2021_paper.pdf) [ICCV'2021] 80 | - Group-Free: [Group-Free 3D Object Detection via Transformers](https://openaccess.thecvf.com/content/ICCV2021/papers/Liu_Group-Free_3D_Object_Detection_via_Transformers_ICCV_2021_paper.pdf) [[Code](https://github.com/zeliu98/Group-Free-3D)] [ICCV'2021] 81 | - RBGNet: [RBGNet: Ray-based Grouping for 3D Object Detection](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RBGNet_Ray-Based_Grouping_for_3D_Object_Detection_CVPR_2022_paper.pdf) [[Code](https://github.com/haiyang-w/rbgnet)] [CVPR'2022] 82 | - FCAF3D: [FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection](https://arxiv.org/pdf/2112.00322.pdf) [[Code](https://github.com/SamsungLabs/fcaf3d)] [ECCV'2022] 83 | 84 | ## Tracking 85 | ### MOT 86 | - Complexer-YOLO: [Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds](http://openaccess.thecvf.com/content_CVPRW_2019/papers/Autonomous%20Driving/Simon_Complexer_YOLO_Real-Time_3D_Object_Detection_and_Tracking_on_Semantic_CVPRW_2019_paper.pdf) [[Code](https://github.com/AI-liu/Complex-YOLO)] [CVPR'2019 Workshop] 87 | - 3DSiamese: [Leveraging Shape Completion for 3D Siamese Tracking](http://openaccess.thecvf.com/content_CVPR_2019/papers/Giancola_Leveraging_Shape_Completion_for_3D_Siamese_Tracking_CVPR_2019_paper.pdf) [[Code](https://github.com/SilvioGiancola/ShapeCompletion3DTracking)] [CVPR'2019] 88 | - AB3DMOT: [A Baseline for 3D Multi-Object Tracking](https://arxiv.org/pdf/1907.03961.pdf) [[Code](https://github.com/xinshuoweng/AB3DMOT)] [arXiv'2019] 89 | - mmMOT: [Robust Multi-Modality Multi-Object Tracking](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Robust_Multi-Modality_Multi-Object_Tracking_ICCV_2019_paper.pdf) [[Code](https://github.com/ZwwWayne/mmMOT)] [ICCV'2019] 90 | - DSM: [End-to-end Learning of Multi-sensor 3D Tracking by Detection](https://arxiv.org/pdf/1806.11534.pdf) [ICRA'2019] 91 | - PointTrackNet: [PointTrackNet: An End-to-End Network for 3-D 92 | Object Detection and Tracking from Point Clouds](https://www.ram-lab.com/papers/2020/wang2020pointtracknet.pdf) [ICRA'2020] 93 | - Mahalanobis-KF: [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://arxiv.org/pdf/2001.05673.pdf) [[Code](https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking)] [arXiv'2020] 94 | - GNN3DMOT: [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with 2D-3D Multi-Feature Learning](https://openaccess.thecvf.com/content_CVPR_2020/papers/Weng_GNN3DMOT_Graph_Neural_Network_for_3D_Multi-Object_Tracking_With_2D-3D_CVPR_2020_paper.pdf) [[Code](https://github.com/xinshuoweng/GNN3DMOT)] [CVPR'2020] 95 | - PC-TCNN: [Tracklet Proposal Network for Multi-Object Tracking on Point Clouds](https://www.ijcai.org/proceedings/2021/0161.pdf) [IJCAI'2021] 96 | - LOGR: [Learnable Online Graph Representations for 3D Multi-Object Tracking](https://arxiv.org/pdf/2104.11747.pdf) [arXiv'2021] 97 | - CenterPoint: [Center-based 3D Object Detection and Tracking](https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Center-Based_3D_Object_Detection_and_Tracking_CVPR_2021_paper.pdf) [[Code](https://github.com/tianweiy/CenterPoint)] [CVPR'2021] 98 | - Immortal-Tracker: [Immortal Tracker: Tracklet Never Dies](https://arxiv.org/pdf/2111.13672.pdf) [[Code](https://github.com/immortaltracker/immortaltracker)] [arXiv'2021] 99 | - SimpleTrack: [SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking](https://arxiv.org/pdf/2111.09621.pdf) [[Code](https://github.com/TuSimple/SimpleTrack)] [arXiv'2021] 100 | ### SOT 101 | - P2B: [P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qi_P2B_Point-to-Box_Network_for_3D_Object_Tracking_in_Point_Clouds_CVPR_2020_paper.pdf) [[Code](https://github.com/HaozheQi/P2B)] [CVPR'2020] 102 | - PTT: [PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds](https://arxiv.org/pdf/2108.06455.pdf) [[Code](https://github.com/shanjiayao/PTT)] [IROS'2021] 103 | - BAT: [Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds](https://openaccess.thecvf.com/content/ICCV2021/papers/Zheng_Box-Aware_Feature_Enhancement_for_Single_Object_Tracking_on_Point_Clouds_ICCV_2021_paper.pdf) [[Code](https://github.com/Ghostish/BAT)] [ICCV'2021] 104 | - LTTR: [3D Object Tracking with Transformer](https://arxiv.org/pdf/2110.14921.pdf) [[Code](https://github.com/3bobo/lttr)] [BMVC'2021] 105 | - V2B: [3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds](https://arxiv.org/pdf/2111.04426.pdf) [[Code](https://github.com/fpthink/V2B)] [NeurIPS'2021] 106 | - PTTR: [PTTR: Relational 3D Point Cloud Object Tracking with Transformer](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_PTTR_Relational_3D_Point_Cloud_Object_Tracking_With_Transformer_CVPR_2022_paper.pdf) [[Code](https://github.com/Jasonkks/PTTR)] [CVPR'2022] 107 | - M2-Tracker: [Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds](https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Beyond_3D_Siamese_Tracking_A_Motion-Centric_Paradigm_for_3D_Single_CVPR_2022_paper.pdf) [[Code](https://github.com/Ghostish/Open3DSOT)] [CVPR'2022] 108 | - STNet: [3D Siamese Transformer Network for Single Object Tracking on Point Clouds](https://arxiv.org/pdf/2207.11995.pdf) [[Code](https://github.com/fpthink/STNet)] [ECCV'2022] 109 | - SMAT: [Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking](https://ieeexplore.ieee.org/document/9899707) [[Code](https://github.com/3bobo/smat)] [RAL] 110 | - STTracker: [STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking](https://ieeexplore.ieee.org/document/10168228) [RAL] 111 | - GLT-T: [GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds](https://arxiv.org/pdf/2211.10927.pdf) [[Code](https://github.com/haooozi/GLT-T)] [AAAI'2023] 112 | - CXTrack: [CXTrack: Improving 3D Point Cloud Tracking with Contextual Information](https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.pdf) [[Code](https://github.com/slothfulxtx/cxtrack3d)] [CVPR'2023] 113 | - SyncTrack: [Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking](https://openaccess.thecvf.com/content/ICCV2023/papers/Ma_Synchronize_Feature_Extracting_and_Matching_A_Single_Branch_Framework_for_ICCV_2023_paper.pdf) [ICCV'2023] 114 | - MBPTrack: [MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors](https://openaccess.thecvf.com/content/ICCV2023/papers/Xu_MBPTrack_Improving_3D_Point_Cloud_Tracking_with_Memory_Networks_and_ICCV_2023_paper.pdf) [[Code](https://github.com/slothfulxtx/MBPTrack3D)] [ICCV'2023] --------------------------------------------------------------------------------