Research map / Detection and segmentation / 3D detection and driving
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add entropy-steered adaptive fusion to deep feature fusion (ablation) | Deep Fusion · Dense fog, hard | AP: 73.93 -> 76.69 | Helped |
| Add entropy-steered adaptive fusion to deep feature fusion (ablation) | Deep Fusion · Snow/rain, hard | AP: 70.51 -> 77.85 | Helped |
| Add entropy-steered adaptive fusion to deep feature fusion (ablation) | Deep Fusion · Light fog, hard | AP: 79.63 -> 84.90 | Helped |
| Add entropy-steered adaptive fusion to deep feature fusion (ablation) | Deep Fusion · Clear, easy | AP: 90.07 -> 89.84 | Hurt |
| Use deep entropy fusion rather than the strongest non-proposed baseline | Object detection on dense fog, hard · Dense fog, hard | AP: 74.96 -> 76.69 | Helped |
| Use multimodal lidar-camera fusion rather than image-only detection in dense fog | Object detection · Dense fog, hard | AP: 67.48 vs 74.96 | Hurt |
| Train on clean data and test on unseen weather using entropy-steered multimodal fusion | Deep Entropy Fusion · Light fog, hard | AP: 84.90 | Helped |
Closest papers by meaning
- L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection (AAAI 2025 · similarity 0.88)
- Resilient Sensor Fusion Under Adverse Sensor Failures via Multi-Modal Expert Fusion (CVPR 2025 · similarity 0.87)
- Equirectangular Point Reconstruction for Domain Adaptive Multimodal 3D Object Detection in Adverse Weather Conditions (AAAI 2025 · similarity 0.86)
- UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather (CVPR 2024 · similarity 0.86)
- Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions (CVPR 2024 · similarity 0.86)
- Layer-Wise Modality Decomposition for Interpretable Multimodal Sensor Fusion (NeurIPS 2025 · similarity 0.86)
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