Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems
This addresses the need for more efficient and accurate extrinsic calibration in advanced driver assistance systems, though it is an incremental improvement over existing offline methods.
The authors tackled the cumbersome and limited-range calibration of surround-view systems by proposing Click-Calib, a pattern-free method that uses user-clicked keypoints to optimize camera poses, achieving superior accuracy and robustness in evaluations on in-house and public datasets.
Surround-View System (SVS) is an essential component in Advanced Driver Assistance System (ADAS) and requires precise calibrations. However, conventional offline extrinsic calibration methods are cumbersome and time-consuming as they rely heavily on physical patterns. Additionally, these methods primarily focus on short-range areas surrounding the vehicle, resulting in lower calibration quality in more distant zones. To address these limitations, we propose Click-Calib, a pattern-free approach for offline SVS extrinsic calibration. Without requiring any special setup, the user only needs to click a few keypoints on the ground in natural scenes. Unlike other offline calibration approaches, Click-Calib optimizes camera poses over a wide range by minimizing reprojection distance errors of keypoints, thereby achieving accurate calibrations at both short and long distances. Furthermore, Click-Calib supports both single-frame and multiple-frame modes, with the latter offering even better results. Evaluations on our in-house dataset and the public WoodScape dataset demonstrate its superior accuracy and robustness compared to baseline methods. Code is available at https://github.com/lwangvaleo/click_calib.