AI RESEARCH
BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations
arXiv CS.CV
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ArXi:2506.02587v2 Announce Type: replace Accurate LiDAR-camera calibration is fundamental to fusing multi-modal perception in autonomous driving and robotic systems. Traditional calibration methods require extensive data collection in controlled environments and cannot compensate for the transformation changes during the vehicle/robot movement. In this paper, we propose the first model that uses bird's-eye view (BEV) features to perform LiDAR camera calibration from raw data, termed