mlsanityData to 7 Oct 2026

RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection

arXiv 2103.10039 ↗First posted 18 Mar 2021cs.CV cs.ROcited by 140

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Abstract (arXiv)

In this paper, we propose an anchor-free single-stage LiDAR-based 3D object detector -- RangeDet. The most notable difference with previous works is that our method is purely based on the range view representation. Compared with the commonly used voxelized or Bird's Eye View (BEV) representations, the range view representation is more compact and without quantization error. Although there are works adopting it for semantic segmentation, its performance in object detection is largely behind voxelized or BEV counterparts. We first analyze the existing range-view-based methods and find two issues overlooked by previous works: 1) the scale variation between nearby and far away objects; 2) the inconsistency between the 2D range image coordinates used in feature extraction and the 3D Cartesian coordinates used in output. Then we deliberately design three components to address these issues in our RangeDet. We test our RangeDet in the large-scale Waymo Open Dataset (WOD). Our best model achieves 72.9/75.9/65.8 3D AP on vehicle/pedestrian/cyclist. These results outperform other range-view-based methods by a large margin (~20 3D AP in vehicle detection), and are overall comparable with the state-of-the-art multi-view-based methods. Codes will be public.

Key facts. Cited by 140 papers in the map (data through 7 Oct 2026). Builds on 41 of its 48 references that are in the map. First posted on arXiv 18 Mar 2021.

Builds on

41 of 48 references.

References · 41

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  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. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  7. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
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  9. 2017Deformable Convolutional Networkscited by 1,481
  10. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  11. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  12. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
29 more
  1. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  2. 2018Path Aggregation Network for Instance Segmentationcited by 796
  3. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  4. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  5. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  6. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  7. 2018Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Drivingcited by 455
  8. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 409
  9. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 366
  10. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  11. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  12. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  13. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  14. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  15. 2020VarifocalNet: An IoU-aware Dense Object Detectorcited by 230
  16. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  17. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  18. 2019Scale-Aware Trident Networks for Object Detectioncited by 159
  19. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  20. 2019LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Drivingcited by 133
  21. 2017An Analysis of Scale Invariance in Object Detection - SNIPcited by 119
  22. 2020Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentationcited by 112
  23. 2016Vehicle Detection from 3D Lidar Using Fully Convolutional Networkcited by 105
  24. 2018SNIPER: Efficient Multi-Scale Trainingcited by 87
  25. 2020Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detectioncited by 78
  26. 2019What You See is What You Get: Exploiting Visibility for 3D Object Detectioncited by 69
  27. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 45; 2023: 40; 2024: 31; 2025: 13; 2026: 2.

140 papers.

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  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. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  4. 2023SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Drivingcited by 310
  5. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  6. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  7. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  8. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  9. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  10. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  11. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 126
  12. 2022DeepInteraction: 3D Object Detection via Modality Interactioncited by 125
38 more
  1. 2023DSVT: Dynamic Sparse Voxel Transformer with Rotated Setscited by 122
  2. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102
  3. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  4. 2022CenterFormer: Center-based Transformer for 3D Object Detectioncited by 99
  5. 2022SWFormer: Sparse Window Transformer for 3D Object Detection in Point Cloudscited by 92
  6. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
  7. 2022Fully Sparse 3D Object Detectioncited by 84
  8. 2023SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detectioncited by 83
  9. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
  10. 2021M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformerscited by 74
  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. 2022AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detectioncited by 70
  15. 2023Cross Modal Transformer: Towards Fast and Robust 3D Object Detectioncited by 68
  16. 2021Multi-Modal 3D Object Detection in Autonomous Driving: a Surveycited by 63
  17. 2022Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmapcited by 59
  18. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  19. 2022GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Cloudscited by 57
  20. 2022Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Imagescited by 56
  21. 2022CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Drivingcited by 56
  22. 2022Polar Parametrization for Vision-based Surround-View 3D Detectioncited by 53
  23. 2023HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Cloudscited by 45
  24. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  25. 2021Immortal Tracker: Tracklet Never Diescited by 42
  26. 2022DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detectioncited by 39
  27. 2023CAPE: Camera View Position Embedding for Multi-View 3D Object Detectioncited by 39
  28. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  29. 2023Learning Occupancy for Monocular 3D Object Detectioncited by 35
  30. 2023FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxelscited by 34
  31. 2023Super Sparse 3D Object Detectioncited by 32
  32. 2023BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregationcited by 31
  33. 2022Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentationcited by 29
  34. 2023Fully Sparse Fusion for 3D Object Detectioncited by 29
  35. 2024SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decompositioncited by 29
  36. 2023Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detectioncited by 28
  37. 2024DrivingRecon: Large 4D Gaussian Reconstruction Model For Autonomous Drivingcited by 27
  38. 2024SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detectioncited by 25

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