Targetless Calibration of Wide-Baseline and Wide-Angle Surround-View Fisheye Cameras Using Cylindrical Projection Model

We propose a novel targetless extrinsic calibration method for wide-baseline and wide-angle fisheye cameras, which are mounted on a driving vehicle for surround view monitoring. Sequences of image frames from three fisheye cameras are obtained, and the object instance and depth around the vehicle are used for calibration. Thus, the proposed method can be applied to online vehicle camera calibration. Fisheye images are first transformed into the cylindrical coordinate system by considering the panoramic formation of the cameras. Then, the state-of-the-art object detection and monocular depth estimation models are applied to the cylindrical images. Vehicle instances matched across different views are reconstructed into 3D point clouds, and their depths are scaled by employing the pose geometry of the front camera. The per-point depths and global scale are then jointly optimized to achieve accurate cross-view alignment and extrinsic calibration. Experiments on both real-world and synthetic video datasets show that the proposed method achieves higher accuracy than COLMAP and DUSt3R under challenging conditions such as wide baselines and low frame rates, without requiring an artificial calibration target.