BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations

TL;DR

BEVCALIB introduces a novel LiDAR-camera calibration method using bird's-eye view features, achieving state-of-the-art accuracy and efficiency in autonomous driving datasets.

Contribution

It is the first model to utilize BEV features for LiDAR-camera calibration directly from raw data, with a geometric feature selector for improved performance.

Findings

Outperforms existing methods by over 47% in translation accuracy on KITTI.

Achieves 82% rotation accuracy improvement over baselines.

Demonstrates robustness under various noise conditions.

Abstract

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 BEVCALIB. To achieve this, we extract camera BEV features and LiDAR BEV features separately and fuse them into a shared BEV feature space. To fully utilize the geometric information from the BEV feature, we introduce a novel feature selector to filter the most important features in the transformation decoder, which reduces memory consumption and enables efficient training. Extensive evaluations on KITTI, NuScenes, and our own dataset demonstrate that BEVCALIB establishes a new state of the art. Under various noise conditions, BEVCALIB outperforms the best baseline in the literature by an average of (47.08%, 82.32%) on KITTI dataset, and (78.17%, 68.29%) on NuScenes dataset, in terms of (translation, rotation), respectively. In the open-source domain, it improves the best reproducible baseline by one order of magnitude. Our code and demo results are available at https://cisl.ucr.edu/BEVCalib.