mlsanityData to 14 Oct 2026

Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective

arXiv 2402.02738 ↗First posted 5 Feb 2024cs.CV cs.LGcited by 4

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Abstract (arXiv)

In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving. However, their robustness against common weather corruption such as fog, rain, snow, and sunlight in the intricate physical world remains underexplored. In this paper, we evaluate the robustness of fusion models from the perspective of fusion strategies on the corrupted dataset. Based on the evaluation, we further propose a concise yet practical fusion strategy to enhance the robustness of the fusion models, namely flexibly weighted fusing features from LiDAR and camera sources to adapt to varying weather scenarios. Experiments conducted on four types of fusion models, each with two distinct lightweight implementations, confirm the broad applicability and effectiveness of the approach.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 35 of its 42 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

35 of 42 references.

References · 35

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  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  3. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  4. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  5. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  6. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
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  10. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 466
  11. 2019PointPainting: Sequential Fusion for 3D Object Detectioncited by 449
  12. 2021FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detectioncited by 447
23 more
  1. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 410
  2. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 336
  3. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 326
  4. 20203D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detectioncited by 176
  5. 2021Multimodal Virtual Point 3D Detectioncited by 166
  6. 2021PENet: Towards Precise and Efficient Image Guided Depth Completioncited by 146
  7. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  8. 2018Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Modelscited by 135
  9. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 129
  10. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 114
  11. 2021Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weathercited by 97
  12. 2020Canadian Adverse Driving Conditions Datasetcited by 87
  13. 2021Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Surveycited by 82
  14. 2023Virtual Sparse Convolution for Multimodal 3D Object Detectioncited by 81
  15. 2022LiDAR Snowfall Simulation for Robust 3D Object Detectioncited by 73
  16. 2022Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detectioncited by 50
  17. 2022Voxel Field Fusion for 3D Object Detectioncited by 50
  18. 2022Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditionscited by 48
  19. 2021Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detectioncited by 43
  20. 2023Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Drivingcited by 37
  21. 2020LIBRE: The Multiple 3D LiDAR Datasetcited by 26
  22. 2023Survey on LiDAR Perception in Adverse Weather Conditionscited by 24
  23. 2022Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancementcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Is Your LiDAR Placement Optimized for 3D Scene Understanding?cited by 26
  2. 2024Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understandingcited by 11
  3. 2026Multi-Sensor Alignment for Weather Simulationscited by 0
  4. 2026SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detectioncited by 0