mlsanityData to 14 Oct 2026

InsFusion: Rethink Instance-level LiDAR-Camera Fusion for 3D Object Detection

arXiv 2509.08374 ↗First posted 10 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

Three-dimensional Object Detection from multi-view cameras and LiDAR is a crucial component for autonomous driving and smart transportation. However, in the process of basic feature extraction, perspective transformation, and feature fusion, noise and error will gradually accumulate. To address this issue, we propose InsFusion, which can extract proposals from both raw and fused features and utilizes these proposals to query the raw features, thereby mitigating the impact of accumulated errors. Additionally, by incorporating attention mechanisms applied to the raw features, it thereby mitigates the impact of accumulated errors. Experiments on the nuScenes dataset demonstrate that InsFusion is compatible with various advanced baseline methods and delivers new state-of-the-art performance for 3D object detection.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 52 of its 55 references that are in the map. First posted on arXiv 10 Sep 2025.

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References · 52

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
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  5. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  6. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  7. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  8. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  9. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
  10. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  11. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  12. 2021BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-Viewcited by 713
38 more
  1. 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 658
  2. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  3. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 572
  4. 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 552
  5. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466
  6. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  7. 2022PETRv2: A Unified Framework for 3D Perception from Multi-Camera Imagescited by 352
  8. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  9. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 327
  10. 2023Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detectioncited by 315
  11. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  12. 2018Orthographic Feature Transform for Monocular 3D Object Detectioncited by 202
  13. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  14. 2021Objects are Different: Flexible Monocular 3D Object Detectioncited by 185
  15. 2022Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detectioncited by 178
  16. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
  17. 2021Multimodal Virtual Point 3D Detectioncited by 166
  18. 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
  19. 2019MVX-Net: Multimodal VoxelNet for 3D Object Detectioncited by 163
  20. 2022Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusioncited by 162
  21. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 156
  22. 2023SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videoscited by 150
  23. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  24. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
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  26. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  27. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  28. 2023Sparse4D v2: Recurrent Temporal Fusion with Sparse Modelcited by 93
  29. 2021AutoShape: Real-Time Shape-Aware Monocular 3D Object Detectioncited by 87
  30. 2021Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detectioncited by 84
  31. 2021Monocular 3D Object Detection: An Extrinsic Parameter Free Approachcited by 69
  32. 2021GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detectioncited by 66
  33. 2023FocalFormer3D : Focusing on Hard Instance for 3D Object Detectioncited by 58
  34. 2024IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detectioncited by 57
  35. 2022STS: Surround-view Temporal Stereo for Multi-view 3D Detectioncited by 53
  36. 2021Progressive Coordinate Transforms for Monocular 3D Object Detectioncited by 52
  37. 2023Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Predictioncited by 49
  38. 2023EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detectioncited by 38

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