mlsanityData to 7 Oct 2026

PIXOR: Real-time 3D Object Detection from Point Clouds

arXiv 1902.06326 ↗First posted 17 Feb 2019cs.CVcited by 358

Not on the map. Why?

Abstract (arXiv)

We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at >28 FPS.

Key facts. Cited by 358 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 102; 2022: 96; 2023: 77; 2024: 41; 2025: 30; 2026: 12.

358 papers.

Sort:

By area:

  1. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  2. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  3. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  4. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  5. 2023UniSim: A Neural Closed-Loop Sensor Simulatorcited by 294
  6. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 248
  7. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  8. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  9. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  10. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  11. 2021Group-Free 3D Object Detection via Transformerscited by 215
  12. 2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
38 more
  1. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  2. 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 192
  3. 2021Multimodal Virtual Point 3D Detectioncited by 166
  4. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  5. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 158
  6. 2023V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perceptioncited by 155
  7. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  8. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  9. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  10. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  11. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  12. 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
  13. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  14. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  15. 2021Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentationcited by 107
  16. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  17. 2023Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusioncited by 105
  18. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102
  19. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  20. 2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
  21. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  22. 2021FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detectioncited by 92
  23. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 91
  24. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
  25. 2022Surface Representation for Point Cloudscited by 84
  26. 2022Fully Sparse 3D Object Detectioncited by 84
  27. 2021Geometry-based Distance Decomposition for Monocular 3D Object Detectioncited by 83
  28. 2021Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloudcited by 81
  29. 2023UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generationcited by 76
  30. 2021M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformerscited by 74
  31. 2022LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perceptioncited by 74
  32. 20213D-MAN: 3D Multi-frame Attention Network for Object Detectioncited by 73
  33. 2022Scene Graph Generation: A Comprehensive Surveycited by 73
  34. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 73
  35. 2022LiDAR Snowfall Simulation for Robust 3D Object Detectioncited by 72
  36. 2021LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detectorcited by 69
  37. 2021Raw High-Definition Radar for Multi-Task Learningcited by 69
  38. 20223D Object Detection from Images for Autonomous Driving: A Surveycited by 66

50 most cited of 358 All 358 (JSON)