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Pillar-based Object Detection for Autonomous Driving

arXiv 2007.10323 ↗First posted 20 Jul 2020cs.CV cs.LG cs.ROcited by 137

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Abstract (arXiv)

We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse, we propose a practical pillar-based approach to fix the imbalance issue caused by anchors. In particular, our algorithm incorporates a cylindrical projection into multi-view feature learning, predicts bounding box parameters per pillar rather than per point or per anchor, and includes an aligned pillar-to-point projection module to improve the final prediction. Our anchor-free approach avoids hyperparameter search associated with past methods, simplifying 3D object detection while significantly improving upon state-of-the-art.

Key facts. Cited by 137 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 32; 2022: 42; 2023: 34; 2024: 16; 2025: 11; 2026: 2.

137 papers.

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  1. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  2. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  3. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  4. 2021Voxel Transformer for 3D Object Detectioncited by 247
  5. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  6. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  7. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  8. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  9. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  10. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  11. 2021Multimodal Virtual Point 3D Detectioncited by 166
  12. 2023V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perceptioncited by 155
38 more
  1. 2022Backdoor Defense via Decoupling the Training Processcited by 149
  2. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  3. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  4. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  5. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  6. 2023DSVT: Dynamic Sparse Voxel Transformer with Rotated Setscited by 122
  7. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  8. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  9. 2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
  10. 2022CenterFormer: Center-based Transformer for 3D Object Detectioncited by 99
  11. 2022SWFormer: Sparse Window Transformer for 3D Object Detection in Point Cloudscited by 92
  12. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
  13. 2022Fully Sparse 3D Object Detectioncited by 84
  14. 2023CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perceptioncited by 81
  15. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  16. 20213D-MAN: 3D Multi-frame Attention Network for Object Detectioncited by 73
  17. 2022Point Density-Aware Voxels for LiDAR 3D Object Detectioncited by 70
  18. 2023Cross Modal Transformer: Towards Fast and Robust 3D Object Detectioncited by 68
  19. 2022MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detectioncited by 65
  20. 2024Volumetric Environment Representation for Vision-Language Navigationcited by 63
  21. 2022Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmapcited by 59
  22. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  23. 2021Object DGCNN: 3D Object Detection using Dynamic Graphscited by 58
  24. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  25. 2021Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perceptioncited by 57
  26. 2022GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Cloudscited by 57
  27. 2023PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Cloudscited by 54
  28. 2022Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detectioncited by 49
  29. 2022Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communicationcited by 47
  30. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  31. 20214D-Net for Learned Multi-Modal Alignmentcited by 44
  32. 2022Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graphcited by 42
  33. 2021To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernelscited by 39
  34. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
  35. 2021LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentationcited by 34
  36. 2023Super Sparse 3D Object Detectioncited by 32
  37. 2023DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Cloudscited by 31
  38. 2022Towards Efficient 3D Object Detection with Knowledge Distillationcited by 29

50 most cited of 137 All 137 (JSON)