CalibBEV: New BEV Alignment Method for LiDAR-Camera CalibrationCalibBEV：基于鸟瞰图对齐的激光雷达-摄像头标定新方法
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On August 3, 2026, Filippo D'Addeo and five co-authors (Lorenzo Cipelli, Adriano Cardace, Emanuele Ghelfi, Andrea Zinelli, Massimo Bertozzi) posted "CalibBEV: LiDAR-Camera Calibration via BEV Alignment" to arXiv (2608.02309v1), proposing a Bird's Eye View (BEV) alignment approach for cross-modal sensor calibration.
CalibBEV unifies LiDAR and camera data into a shared 3D spatial representation by extracting sensor-wise BEV features from each modality with domain-specific architectures, then estimates the calibration matrix via a two-step process, beginning with an implicit alignment that directly regresses a coarse calibration matrix.
Accurate LiDAR-camera calibration underpins sensor fusion in autonomous-driving perception stacks, so a more robust, learning-based calibration method could reduce manual calibration overhead for AV and ADAS systems that rely on multi-sensor 3D perception.