TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

LiDAR and camera are two important sensors for 3D object detection in autonomous driving. We provide extensive experiments to demonstrate its robustness against degenerated image quality and calibration errors.

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• 1 Despite the increasing popularity of sensor fusion in this field, the robustness against inferior image conditions, e.g., bad illumination and sensor misalignment, is under-explored.
• 2 Existing fusion methods are easily affected by such conditions, mainly due to a hard association of LiDAR points and image pixels, established by calibration matrices.
• 3 We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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