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, accurate calibration matrix.

Our method demonstrates robustness in LiDAR-camera calibration. Starting from different initial mis-calibration states, CalibBEV consistently recovers a close approximation of the true calibration matrix, showcasing the method's ability to handle various initialization conditions while achieving accurate and reliable calibration results.