CalibBEV: LiDAR-Camera Calibration via BEV Alignment

• URL: http://arxiv.org/abs/2608.02309v1
• Date: Mon, 03 Aug 2026 14:34:44 GMT
• Title: CalibBEV: LiDAR-Camera Calibration via BEV Alignment
• Abstract summary: CalibBEV is an alignment approach for LiDAR-camera calibration.<n>Our method unifies LiDAR and camera data into a shared 3D spatial representation.<n>CalibBEV significantly outperforms prior point-to-pixel matching methods.
• Score: 5.0138688681521435
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present CalibBEV, a novel Bird's Eye View (BEV) alignment approach for LiDAR-camera calibration. Our method unifies LiDAR and camera data into a shared 3D spatial representation, enabling accurate and robust cross-modal calibration. CalibBEV extracts sensor-wise BEV features from each modality using domain-specific architectures and estimates the calibration matrix through a two-step alignment process. First, we perform an implicit alignment by regressing a coarse calibration matrix directly from the BEV features. To ease this alignment, we enforce semantic consistency between BEV representations across modalities using a contrastive loss inspired by CLIP, guiding both networks toward a unified feature space. In the second step, we leverage our BEV formulation to explicitly align the features of one modality with the other, refining the initial coarse estimate into a final, more accurate calibration matrix. CalibBEV significantly outperforms prior point-to-pixel matching methods, achieving state-of-the-art calibration accuracy. On the KITTI and nuScenes benchmarks, our method reduces the Relative Rotation Error (RRE) by 51% and 68%, and the Relative Translation Error (RTE) by 80% and 91%, respectively, compared to previous methods.

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