mlsanityData to 7 Oct 2026

RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection

arXiv 2106.13365 ↗First posted 25 Jun 2021cs.CVcited by 115

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Abstract (arXiv)

The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by new LiDARs. These larger detection ranges require more efficient and accurate detection models. Towards this goal, we propose Range Sparse Net (RSN), a simple, efficient, and accurate 3D object detector in order to tackle real time 3D object detection in this extended detection regime. RSN predicts foreground points from range images and applies sparse convolutions on the selected foreground points to detect objects. The lightweight 2D convolutions on dense range images results in significantly fewer selected foreground points, thus enabling the later sparse convolutions in RSN to efficiently operate. Combining features from the range image further enhance detection accuracy. RSN runs at more than 60 frames per second on a 150m x 150m detection region on Waymo Open Dataset (WOD) while being more accurate than previously published detectors. As of 11/2020, RSN is ranked first in the WOD leaderboard based on the APH/LEVEL 1 metrics for LiDAR-based pedestrian and vehicle detection, while being several times faster than alternatives.

Key facts. Cited by 115 papers in the map (data through 7 Oct 2026). Builds on 38 of its 45 references that are in the map. First posted on arXiv 25 Jun 2021.

Builds on

38 of 45 references.

References · 38

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  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
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  12. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
26 more
  1. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  2. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  3. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  4. 2019Objects as Pointscited by 1,172
  5. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  6. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  7. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  8. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  9. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  10. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  11. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 437
  12. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  13. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  14. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  15. 2017Submanifold Sparse Convolutional Networkscited by 223
  16. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  17. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  18. 2019LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Drivingcited by 133
  19. 2019IoU Loss for 2D/3D Object Detectioncited by 128
  20. 2020Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detectioncited by 78
  21. 2016Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networkscited by 72
  22. 2020AFDet: Anchor Free One Stage 3D Object Detectioncited by 63
  23. 2019StarNet: Targeted Computation for Object Detection in Point Cloudscited by 46
  24. 2020Improving 3D Object Detection through Progressive Population Based Augmentationcited by 41
  25. 2020RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representationcited by 37
  26. 2020PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challengescited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 37; 2023: 46; 2024: 18; 2025: 7; 2026: 1.

115 papers.

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  1. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  2. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  3. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  4. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  5. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  6. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  7. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  8. 2023Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Drivingcited by 168
  9. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  10. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  11. 2022CenterFormer: Center-based Transformer for 3D Object Detectioncited by 99
  12. 2022SWFormer: Sparse Window Transformer for 3D Object Detection in Point Cloudscited by 92
38 more
  1. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
  2. 2022Fully Sparse 3D Object Detectioncited by 84
  3. 2023CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perceptioncited by 81
  4. 2022LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perceptioncited by 74
  5. 2022LargeKernel3D: Scaling up Kernels in 3D Sparse CNNscited by 71
  6. 2023Cross Modal Transformer: Towards Fast and Robust 3D Object Detectioncited by 68
  7. 2024Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detectioncited by 66
  8. 2022MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detectioncited by 65
  9. 2023Exploring Recurrent Long-term Temporal Fusion for Multi-view 3D Perceptioncited by 65
  10. 2022PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functionscited by 62
  11. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  12. 2023FocalFormer3D : Focusing on Hard Instance for 3D Object Detectioncited by 58
  13. 2022GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Cloudscited by 57
  14. 2022Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Imagescited by 56
  15. 2023PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Cloudscited by 54
  16. 2022Polar Parametrization for Vision-based Surround-View 3D Detectioncited by 53
  17. 2023HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Cloudscited by 45
  18. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  19. 2023FastPillars: A Deployment-friendly Pillar-based 3D Detectorcited by 39
  20. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  21. 2023FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxelscited by 34
  22. 2023Super Sparse 3D Object Detectioncited by 32
  23. 2023BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregationcited by 31
  24. 2022CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detectioncited by 30
  25. 2022Towards Efficient 3D Object Detection with Knowledge Distillationcited by 29
  26. 2023Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detectioncited by 28
  27. 2022LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detectioncited by 27
  28. 2024SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detectioncited by 25
  29. 2024MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detectioncited by 23
  30. 2021BoxeR: Box-Attention for 2D and 3D Transformerscited by 22
  31. 2023Robots That Can See: Leveraging Human Pose for Trajectory Predictioncited by 22
  32. 2023OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detectioncited by 22
  33. 2024LISO: Lidar-only Self-Supervised 3D Object Detectioncited by 22
  34. 2022ConQueR: Query Contrast Voxel-DETR for 3D Object Detectioncited by 21
  35. 2023PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformercited by 21
  36. 2022Improving the Intra-class Long-tail in 3D Detection via Rare Example Miningcited by 19
  37. 2024DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentationcited by 18
  38. 2023Human-centric Scene Understanding for 3D Large-scale Scenarioscited by 17

50 most cited of 115 All 115 (JSON)