无需标定板，点击地面点即可完成全景摄像头精准校准。
Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems
- 用户在自然场景中点击地面若干关键点，系统自动优化相机位姿。
- 在近处和远处均实现高精度校准，相比基线方法误差降低37%以上。
- 支持单帧与多帧模式，适合车载视觉系统研发与调试人员使用。
全景视觉系统（SVS）是高级驾驶辅助系统（ADAS）的核心组件，需精确的外参标定。传统离线标定方法依赖物理标定板，过程繁琐且仅适用于车辆周边短距区域，远距离校准精度不足。为此，本文提出无标定板的离线标定方法 Click-Calib：用户只需在自然场景中点击地面若干关键点，系统通过最小化关键点重投影误差，在大范围内优化相机姿态，实现远近均优的校准效果。该方法支持单帧与多帧模式，后者性能更佳。在自建数据集及公开的 WoodScape 数据集上的评估表明，其精度和鲁棒性显著优于现有基线方法。代码已开源：https://github.com/lwangvaleo/click_calib。
原文摘要 · Abstract (English)
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.
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