mlsanityData to 7 Oct 2026

LiDAR R-CNN: An Efficient and Universal 3D Object Detector

arXiv 2103.15297 ↗First posted 29 Mar 2021cs.CVcited by 129

Not on the map. Why?

Abstract (arXiv)

LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that can generally improve any existing 3D detector. To fulfill the real-time and high precision requirement in practice, we resort to point-based approach other than the popular voxel-based approach. However, we find an overlooked issue in previous work: Naively applying point-based methods like PointNet could make the learned features ignore the size of proposals. To this end, we analyze this problem in detail and propose several methods to remedy it, which bring significant performance improvement. Comprehensive experimental results on real-world datasets like Waymo Open Dataset (WOD) and KITTI dataset with various popular detectors demonstrate the universality and superiority of our LiDAR R-CNN. In particular, based on one variant of PointPillars, our method could achieve new state-of-the-art results with minor cost. Codes will be released at https://github.com/tusimple/LiDAR_RCNN .

Key facts. Cited by 129 papers in the map (data through 7 Oct 2026). Builds on 49 of its 55 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

49 of 55 references.

References · 49

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  5. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  7. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  8. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  9. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  10. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  11. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  12. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
37 more
  1. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  2. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  3. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 455
  4. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  5. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 366
  6. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  7. 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
  8. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  9. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
  10. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  11. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  12. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  13. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  14. 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
  15. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  16. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  17. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  18. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  19. 2019LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Drivingcited by 133
  20. 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
  21. 2019Fast Point R-CNNcited by 119
  22. 2020ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votescited by 119
  23. 2020Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentationcited by 112
  24. 2015Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Imagescited by 111
  25. 2016Vehicle Detection from 3D Lidar Using Fully Convolutional Networkcited by 105
  26. 2020Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detectioncited by 78
  27. 2020HDNET: Exploiting HD Maps for 3D Object Detectioncited by 78
  28. 2020SSN: Shape Signature Networks for Multi-class Object Detection from Point Cloudscited by 74
  29. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  30. 2020AFDet: Anchor Free One Stage 3D Object Detectioncited by 63
  31. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
  32. 2018IPOD: Intensive Point-based Object Detector for Point Cloudcited by 46
  33. 2019StarNet: Targeted Computation for Object Detection in Point Cloudscited by 46
  34. 2020RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representationcited by 37
  35. 2018YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloudcited by 32
  36. 2020DMLO: Deep Matching LiDAR Odometrycited by 20
  37. 2019Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point cloudscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 39; 2023: 40; 2024: 27; 2025: 12; 2026: 4.

129 papers.

Sort:

By area:

  1. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  2. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  3. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
  4. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  5. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  6. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 262
  7. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  8. 2023ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understandingcited by 199
  9. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  10. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  11. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  12. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
38 more
  1. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  2. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 128
  3. 2023DSVT: Dynamic Sparse Voxel Transformer with Rotated Setscited by 122
  4. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102
  5. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  6. 2022CenterFormer: Center-based Transformer for 3D Object Detectioncited by 99
  7. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
  8. 2022Fully Sparse 3D Object Detectioncited by 84
  9. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
  10. 2023LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusioncited by 80
  11. 2022LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perceptioncited by 74
  12. 2023FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformercited by 72
  13. 2022LargeKernel3D: Scaling up Kernels in 3D Sparse CNNscited by 71
  14. 2023Cross Modal Transformer: Towards Fast and Robust 3D Object Detectioncited by 68
  15. 2022MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detectioncited by 65
  16. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  17. 2023FocalFormer3D : Focusing on Hard Instance for 3D Object Detectioncited by 58
  18. 2022GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Cloudscited by 57
  19. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  20. 2022Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detectioncited by 49
  21. 2022Patchwork++: Fast and Robust Ground Segmentation Solving Partial Under-Segmentation Using 3D Point Cloudcited by 45
  22. 2023HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Cloudscited by 45
  23. 2021Immortal Tracker: Tracklet Never Diescited by 42
  24. 2022Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graphcited by 42
  25. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  26. 2023FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxelscited by 34
  27. 2022PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detectioncited by 33
  28. 2024GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detectioncited by 33
  29. 2023Super Sparse 3D Object Detectioncited by 32
  30. 2023OcTr: Octree-based Transformer for 3D Object Detectioncited by 32
  31. 2023BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregationcited by 31
  32. 2022HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detectioncited by 30
  33. 2023Fully Sparse Fusion for 3D Object Detectioncited by 29
  34. 2022Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detectioncited by 26
  35. 2024SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detectioncited by 25
  36. 2023RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detectioncited by 23
  37. 2021BoxeR: Box-Attention for 2D and 3D Transformerscited by 22
  38. 2023OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detectioncited by 22

50 most cited of 129 All 129 (JSON)