mlsanityData to 14 Oct 2026

EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

arXiv 2007.08856 ↗First posted 17 Jul 2020cs.CVcited by 164

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Abstract (arXiv)

In this paper, we aim at addressing two critical issues in the 3D detection task, including the exploitation of multiple sensors~(namely LiDAR point cloud and camera image), as well as the inconsistency between the localization and classification confidence. To this end, we propose a novel fusion module to enhance the point features with semantic image features in a point-wise manner without any image annotations. Besides, a consistency enforcing loss is employed to explicitly encourage the consistency of both the localization and classification confidence. We design an end-to-end learnable framework named EPNet to integrate these two components. Extensive experiments on the KITTI and SUN-RGBD datasets demonstrate the superiority of EPNet over the state-of-the-art methods. Codes and models are available at: urlhttps://github.com/happinesslz/EPNet.

Key facts. Cited by 164 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jul 2020.

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Citing papers per year: 2021: 21; 2022: 41; 2023: 42; 2024: 33; 2025: 18; 2026: 9.

164 papers.

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  1. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466
  2. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  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. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  6. 2021Multimodal Virtual Point 3D Detectioncited by 166
  7. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  8. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  9. 2022DeepInteraction: 3D Object Detection via Modality Interactioncited by 125
  10. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 114
  11. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 94
  12. 2022Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completioncited by 82
38 more
  1. 2023LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusioncited by 80
  2. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 76
  3. 2023UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representationcited by 74
  4. 2022AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detectioncited by 70
  5. 2022CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformercited by 67
  6. 2021GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detectioncited by 66
  7. 2021Multi-Modal 3D Object Detection in Autonomous Driving: a Surveycited by 63
  8. 2022MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detectioncited by 62
  9. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  10. 2022Transformation-Equivariant 3D Object Detection for Autonomous Drivingcited by 52
  11. 2022Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detectioncited by 50
  12. 2022Voxel Field Fusion for 3D Object Detectioncited by 50
  13. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  14. 2021VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusioncited by 48
  15. 2024Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Predictioncited by 48
  16. 2023UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye Viewcited by 43
  17. 2022Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graphcited by 42
  18. 2025Multimodal Fusion and Vision-Language Models: A Survey for Robot Visioncited by 40
  19. 2021LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentationcited by 34
  20. 2023GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detectioncited by 34
  21. 2024GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detectioncited by 33
  22. 2022CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detectioncited by 32
  23. 2023TR3D: Towards Real-Time Indoor 3D Object Detectioncited by 32
  24. 2022HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detectioncited by 31
  25. 2023BEVFusion4D: Learning LiDAR-Camera Fusion Under Bird's-Eye-View via Cross-Modality Guidance and Temporal Aggregationcited by 31
  26. 2022CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detectioncited by 30
  27. 2023Fully Sparse Fusion for 3D Object Detectioncited by 29
  28. 2024Vision-based 3D occupancy prediction in autonomous driving: a review and outlookcited by 29
  29. 2023Uni3DETR: Unified 3D Detection Transformercited by 27
  30. 2021Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehiclescited by 26
  31. 2021Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Drivingcited by 26
  32. 2022Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detectioncited by 26
  33. 2024DVLO: Deep Visual-LiDAR Odometry with Local-to-Global Feature Fusion and Bi-Directional Structure Alignmentcited by 26
  34. 2024Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Propertiescited by 26
  35. 2024SparseLIF: High-Performance Sparse LiDAR-Camera Fusion for 3D Object Detectioncited by 25
  36. 2024A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directionscited by 25
  37. 2022GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimationcited by 24
  38. 2022DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detectioncited by 20

50 most cited of 164 All 164 (JSON)