CalibBEV: LiDAR-Camera Calibration via BEV Alignment

2026-08-03

AI Summary

This work addresses the challenging problem of cross-modal calibration between LiDAR and cameras by proposing the first bird’s-eye-view (BEV)-based alignment framework. The method unifies multimodal data into a shared BEV space and employs a two-stage optimization strategy: it first implicitly regresses coarse calibration parameters and then explicitly aligns cross-modal features, enhanced by a CLIP-inspired contrastive loss to enforce semantic consistency. By integrating domain-specific BEV feature extraction with contrastive learning constraints, the approach significantly outperforms existing methods, achieving state-of-the-art calibration accuracy. On the KITTI and nuScenes benchmarks, it reduces relative rotation error by 51% and 68%, and translation error by 80% and 91%, respectively.

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.

Problem

• LiDAR-camera calibration
• BEV alignment
• cross-modal calibration
• sensor fusion

Innovation

• BEV alignment
• LiDAR-camera calibration
• cross-modal representation
• contrastive learning
• sensor fusion