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Multi-View 3D Object Detection Network for Autonomous Driving

arXiv 1611.07759 ↗First posted 23 Nov 2016cs.CVcited by 721

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Abstract (arXiv)

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view representation. The network is composed of two subnetworks: one for 3D object proposal generation and another for multi-view feature fusion. The proposal network generates 3D candidate boxes efficiently from the bird's eye view representation of 3D point cloud. We design a deep fusion scheme to combine region-wise features from multiple views and enable interactions between intermediate layers of different paths. Experiments on the challenging KITTI benchmark show that our approach outperforms the state-of-the-art by around 25% and 30% AP on the tasks of 3D localization and 3D detection. In addition, for 2D detection, our approach obtains 10.3% higher AP than the state-of-the-art on the hard data among the LIDAR-based methods.

Key facts. Cited by 721 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2016.

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Citing papers per year: 2021: 166; 2022: 199; 2023: 153; 2024: 95; 2025: 77; 2026: 31.

721 papers.

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  1. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
  2. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  3. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  4. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  5. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  6. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 461
  7. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 384
  8. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
  9. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  10. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  11. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  12. 2022DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detectioncited by 270
38 more
  1. 2021Is Pseudo-Lidar needed for Monocular 3D Object detection?cited by 248
  2. 2021Voxel Transformer for 3D Object Detectioncited by 247
  3. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  4. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  5. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  6. 2021Group-Free 3D Object Detection via Transformerscited by 215
  7. 2021Trustworthy AI: From Principles to Practicescited by 206
  8. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  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. 2021PandaSet: Advanced Sensor Suite Dataset for Autonomous Drivingcited by 186
  12. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 175
  13. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  14. 2021Multimodal Virtual Point 3D Detectioncited by 166
  15. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  16. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 158
  17. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 155
  18. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  19. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  20. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  21. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  22. 2021Delving into Localization Errors for Monocular 3D Object Detectioncited by 138
  23. 2022Multi-View Transformer for 3D Visual Groundingcited by 138
  24. 2024RVT-2: Learning Precise Manipulation from Few Demonstrationscited by 135
  25. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  26. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  27. 2022Vision-Centric BEV Perception: A Surveycited by 126
  28. 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
  29. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  30. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  31. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 112
  32. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  33. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  34. 2022CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attentioncited by 103
  35. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102
  36. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  37. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  38. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99

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