mlsanityData to 14 Oct 2026

Fully Sparse Fusion for 3D Object Detection

arXiv 2304.12310 ↗First posted 24 Apr 2023cs.CVcited by 29

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Abstract (arXiv)

Currently prevalent multimodal 3D detection methods are built upon LiDAR-based detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not suitable for long-range detection. Fully sparse architecture is gaining attention as they are highly efficient in long-range perception. In this paper, we study how to effectively leverage image modality in the emerging fully sparse architecture. Particularly, utilizing instance queries, our framework integrates the well-studied 2D instance segmentation into the LiDAR side, which is parallel to the 3D instance segmentation part in the fully sparse detector. This design achieves a uniform query-based fusion framework in both the 2D and 3D sides while maintaining the fully sparse characteristic. Extensive experiments showcase state-of-the-art results on the widely used nuScenes dataset and the long-range Argoverse 2 dataset. Notably, the inference speed of the proposed method under the long-range LiDAR perception setting is 2.7 × faster than that of other state-of-the-art multimodal 3D detection methods. Code will be released at urlhttps://github.com/BraveGroup/FullySparseFusion.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 69 of its 82 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

69 of 82 references.

References · 69

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2017Mask R-CNNcited by 7,277
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  6. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  7. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  8. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  9. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  10. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  11. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  12. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
38 more
  1. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  2. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
  3. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  4. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  5. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 707
  6. 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 658
  7. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  8. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  9. 2022BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detectioncited by 572
  10. 2022PETR: Position Embedding Transformation for Multi-View 3D Object Detectioncited by 552
  11. 2019Hybrid Task Cascade for Instance Segmentationcited by 481
  12. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466
  13. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  14. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  15. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 368
  16. 2019Class-balanced Grouping and Sampling for Point Cloud 3D Object Detectioncited by 350
  17. 2022BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detectioncited by 341
  18. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  19. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 327
  20. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 326
  21. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  22. 2019M3D-RPN: Monocular 3D Region Proposal Network for Object Detectioncited by 252
  23. 2022DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detectioncited by 224
  24. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 222
  25. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  26. 2022FUTR3D: A Unified Sensor Fusion Framework for 3D Detectioncited by 194
  27. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  28. 2021Objects are Different: Flexible Monocular 3D Object Detectioncited by 185
  29. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
  30. 2021Multimodal Virtual Point 3D Detectioncited by 166
  31. 2020EPNet: Enhancing Point Features with Image Semantics for 3D Object Detectioncited by 164
  32. 2019Stereo R-CNN based 3D Object Detection for Autonomous Drivingcited by 163
  33. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 156
  34. 2020CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detectioncited by 147
  35. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  36. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  37. 2021LiDAR R-CNN: An Efficient and Universal 3D Object Detectorcited by 129
  38. 2022DeepInteraction: 3D Object Detection via Modality Interactioncited by 125

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 9; 2025: 7; 2026: 8.

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  1. 2024Enhancing End-to-End Autonomous Driving with Latent World Modelcited by 166
  2. 2025End-to-End Driving with Online Trajectory Evaluation via BEV World Modelcited by 151
  3. 2023UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representationcited by 74
  4. 2024MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detectioncited by 23
  5. 2024DeepInteraction++: Multi-Modality Interaction for Autonomous Drivingcited by 15
  6. 2023Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object Detectioncited by 11
  7. 2024Progressive Multi-Modal Fusion for Robust 3D Object Detectioncited by 11
  8. 2023Radars for Autonomous Driving: A Review of Deep Learning Methods and Challengescited by 10
  9. 2024FlatFusion: Delving into Details of Sparse Transformer-based Camera-LiDAR Fusion for Autonomous Drivingcited by 9
  10. 2024SparseFusion: Efficient Sparse Multi-Modal Fusion Framework for Long-Range 3D Perceptioncited by 8
  11. 2025How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentationcited by 8
  12. 2025Self-Supervised Sparse Sensor Fusion for Long Range Perceptioncited by 8
17 more
  1. 2023Towards Long-Range 3D Object Detection for Autonomous Vehiclescited by 5
  2. 2024PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interestcited by 4
  3. 2024MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimationcited by 3
  4. 2026CCF: Complementary Collaborative Fusion for Domain Generalized Multi-Modal 3D Object Detectioncited by 3
  5. 2026DSERT-RoLL: Robust Multi-Modal Perception for Diverse Driving Conditions with Stereo Event-RGB-Thermal Cameras, 4D Radar, and Dual-LiDARcited by 3
  6. 2023What You See Is What You Detect: Towards better Object Densification in 3D detectioncited by 1
  7. 2024FSMDet: Vision-guided feature diffusion for fully sparse 3D detectorcited by 1
  8. 2026MambaFusion: Adaptive State-Space Fusion for Multimodal 3D Object Detectioncited by 1
  9. 2025Reliable Multimodal Learning Via Multi-Level Adaptive DeConfusioncited by 0
  10. 2025Rethink 3D Object Detection from Physical Worldcited by 0
  11. 2025Co-Win: Joint Object Detection and Instance Segmentation in LiDAR Point Clouds via Collaborative Window Processingcited by 0
  12. 2025CrossRay3D: Geometry and Distribution Guidance for Efficient Multimodal 3D Detectioncited by 0
  13. 2026Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classificationcited by 0
  14. 2026Information Coordination as a Bridge: A Neuro-Symbolic Architecture for Reliable Autonomous Driving Scene Understandingcited by 0
  15. 2026GaussianFusion: Unified 3D Gaussian Representation for Multi-Modal Fusion Perceptioncited by 0
  16. 2026DeGuNet: Depth-Guided Ultra-Compact Backbones for Efficient LiDAR-Camera 3D Detectioncited by 0
  17. 2026RSFusionDet: Underwater RGB-Sonar Multimodal Object Detectioncited by 0