Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems

TL;DR

Click-Calib introduces a pattern-free, click-based calibration method for surround-view systems that achieves high accuracy across short and long distances without special setups.

Contribution

It presents a novel offline calibration approach that minimizes reprojection errors using user clicks, improving accuracy and robustness over existing methods.

Findings

Outperforms baseline methods in accuracy and robustness.

Effective in both single-frame and multi-frame modes.

Validated on in-house and public datasets.

Abstract

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.