mlsanityData to 14 Oct 2026

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

arXiv 1812.04244 ↗First posted 11 Dec 2018cs.CVcited by 941

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Abstract (arXiv)

In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to learn better local spatial features, which is combined with global semantic features of each point learned in stage-1 for accurate box refinement and confidence prediction. Extensive experiments on the 3D detection benchmark of KITTI dataset show that our proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input. The code is available at https://github.com/sshaoshuai/PointRCNN.

Key facts. Cited by 941 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2018.

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Citing papers per year: 2021: 158; 2022: 240; 2023: 224; 2024: 155; 2025: 122; 2026: 42.

941 papers.

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  1. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  2. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 572
  3. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466
  4. 2021FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detectioncited by 447
  5. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  6. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  7. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  8. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 327
  9. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  10. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  11. 2023OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Predictioncited by 293
  12. 2022DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detectioncited by 273
38 more
  1. 2021Voxel Transformer for 3D Object Detectioncited by 247
  2. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  3. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  4. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 222
  5. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  6. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  7. 2021Group-Free 3D Object Detection via Transformerscited by 215
  8. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  9. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  10. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  11. 2021PandaSet: Advanced Sensor Suite Dataset for Autonomous Drivingcited by 190
  12. 2021Objects are Different: Flexible Monocular 3D Object Detectioncited by 185
  13. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 169
  14. 2021Multimodal Virtual Point 3D Detectioncited by 166
  15. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 159
  16. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 156
  17. 2022M2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representationcited by 156
  18. 2023EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AIcited by 147
  19. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 145
  20. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  21. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  22. 2021Delving into Localization Errors for Monocular 3D Object Detectioncited by 140
  23. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  24. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  25. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  26. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 129
  27. 2022Vision-Centric BEV Perception: A Surveycited by 126
  28. 2023Spherical Transformer for LiDAR-based 3D Recognitioncited by 125
  29. 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
  30. 2023DSVT: Dynamic Sparse Voxel Transformer with Rotated Setscited by 122
  31. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  32. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  33. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 114
  34. 2022MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformercited by 109
  35. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  36. 2021Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentationcited by 107
  37. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  38. 2021DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistencycited by 103

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