mlsanityData to 7 Oct 2026

Fast Point R-CNN

arXiv 1908.02990 ↗First posted 8 Aug 2019cs.CVcited by 119

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Abstract (arXiv)

We present a unified, efficient and effective framework for point-cloud based 3D object detection. Our two-stage approach utilizes both voxel representation and raw point cloud data to exploit respective advantages. The first stage network, with voxel representation as input, only consists of light convolutional operations, producing a small number of high-quality initial predictions. Coordinate and indexed convolutional feature of each point in initial prediction are effectively fused with the attention mechanism, preserving both accurate localization and context information. The second stage works on interior points with their fused feature for further refining the prediction. Our method is evaluated on KITTI dataset, in terms of both 3D and Bird's Eye View (BEV) detection, and achieves state-of-the-arts with a 15FPS detection rate.

Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Aug 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 39; 2022: 36; 2023: 19; 2024: 14; 2025: 10; 2026: 1.

119 papers.

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  1. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  2. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  3. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  4. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  5. 2021PandaSet: Advanced Sensor Suite Dataset for Autonomous Drivingcited by 186
  6. 2021Multimodal Virtual Point 3D Detectioncited by 166
  7. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  8. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  9. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  10. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  11. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  12. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
38 more
  1. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102
  2. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  3. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
  4. 2021M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformerscited by 74
  5. 2023FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformercited by 72
  6. 2021PointCutMix: Regularization Strategy for Point Cloud Classificationcited by 69
  7. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  8. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  9. 2023FocalFormer3D : Focusing on Hard Instance for 3D Object Detectioncited by 58
  10. 2022Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoderscited by 57
  11. 2024IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detectioncited by 57
  12. 2022Polar Parametrization for Vision-based Surround-View 3D Detectioncited by 53
  13. 2022SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detectioncited by 52
  14. 2024SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detectioncited by 52
  15. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  16. 20213D Object Tracking with Transformercited by 49
  17. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  18. 2021VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusioncited by 48
  19. 2021HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detectioncited by 47
  20. 2022Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditionscited by 47
  21. 2023HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Cloudscited by 45
  22. 2022Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graphcited by 42
  23. 2022DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detectioncited by 39
  24. 2024LION: Linear Group RNN for 3D Object Detection in Point Cloudscited by 38
  25. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  26. 2022PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detectioncited by 33
  27. 2021FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detectioncited by 29
  28. 2022Towards Efficient 3D Object Detection with Knowledge Distillationcited by 29
  29. 2021Robustness of Object Detectors in Degrading Weather Conditionscited by 28
  30. 2025CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridizationcited by 27
  31. 2022DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detectioncited by 25
  32. 2024SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detectioncited by 25
  33. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  34. 2021From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decodercited by 23
  35. 2021DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentationcited by 22
  36. 2021Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detectioncited by 20
  37. 2023Language-Guided 3D Object Detection in Point Cloud for Autonomous Drivingcited by 19
  38. 2021Revisiting 3D Object Detection From an Egocentric Perspectivecited by 18

50 most cited of 119 All 119 (JSON)