Weifeng Xu

Camera–LiDAR bird's-eye-view fusion can mistake corrupted visual texture for useful evidence when the camera stream degrades. DARFusion recasts fusion as modality trust estimation: learned reliability cues calibrate both feature streams, and calibrated features, task-driven contexts and a compact degradation descriptor derived from camera BEV features decide how much camera evidence to use. It holds 68.4 mAP and 71.5 NDS on the clean nuScenes validation set and beats the reproduced BEVFusion baseline by 1.41 and 1.31 mAP on real rain and night subsets.

Camera–LiDAR bird's-eye-view fusion can mistake corrupted visual texture for useful evidence when the camera stream degrades. DARFusion recasts fusion as modality trust estimation: learned reliability cues calibrate both feature streams, and calibrated features, task-driven contexts and a compact degradation descriptor derived from camera BEV features decide how much camera evidence to use. It holds 68.4 mAP and 71.5 NDS on the clean nuScenes validation set and beats the reproduced BEVFusion baseline by 1.41 and 1.31 mAP on real rain and night subsets.