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Deep Continuous Fusion for Multi-Sensor 3D Object Detection

arXiv 2012.10992 ↗First posted 20 Dec 2020cs.CVcited by 263

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Abstract (arXiv)

In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.

Key facts. Cited by 263 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 65; 2022: 77; 2023: 65; 2024: 26; 2025: 21; 2026: 9.

263 papers.

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  1. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  2. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 485
  3. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466
  4. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 377
  5. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  6. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
  7. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  8. 2021Voxel Transformer for 3D Object Detectioncited by 247
  9. 2021TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditionscited by 244
  10. 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
  11. 2021PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detectioncited by 232
  12. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
38 more
  1. 2021Group-Free 3D Object Detection via Transformerscited by 215
  2. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 212
  3. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  4. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  5. 2021Multimodal Virtual Point 3D Detectioncited by 166
  6. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  7. 2021What Makes Multi-modal Learning Better than Single (Provably)cited by 153
  8. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 145
  9. 2021FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimationcited by 144
  10. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  11. 2022Learning from All Vehiclescited by 135
  12. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  13. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  14. 2022Vision-Centric BEV Perception: A Surveycited by 126
  15. 2022Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipecited by 122
  16. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  17. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 114
  18. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  19. 20213D Object Detection for Autonomous Driving: A Surveycited by 106
  20. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 103
  21. 2022Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Cloudscited by 102
  22. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  23. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
  24. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 94
  25. 2022Surface Representation for Point Cloudscited by 84
  26. 2022SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformercited by 83
  27. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
  28. 2021Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimationcited by 80
  29. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  30. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 76
  31. 2021M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformerscited by 74
  32. 2023FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformercited by 73
  33. 2022AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detectioncited by 70
  34. 2021Monocular 3D Object Detection: An Extrinsic Parameter Free Approachcited by 69
  35. 20223D Object Detection from Images for Autonomous Driving: A Surveycited by 66
  36. 2021Multi-Modal 3D Object Detection in Autonomous Driving: a Surveycited by 63
  37. 2022MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detectioncited by 62
  38. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60

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