一种鲁棒的PTZ相机标定方法，可高效准确地估算任意视角参数。
PTZ-Calib: Robust Pan-Tilt-Zoom Camera Calibration
- 分离式两阶段标定：离线构建参考图集并用PTZ-IBA算法自动标定
- 在线阶段将新视角标定转化为重定位问题，兼顾精度与效率
- 支持地理坐标对齐，适用于真实场景中的多视角监控系统
本文提出PTZ-Calib，一种鲁棒的两阶段PTZ相机标定方法，可高效且准确地估计任意视角下的相机参数。该方法包含离线与在线两个阶段。离线阶段首先均匀选取一组具有充分重叠的参考图像，覆盖完整的360°视场；随后利用新型PTZ增量式捆绑调整（PTZ-IBA）算法，在局部坐标系下自动完成相机标定。此外，为便于实际应用，可结合额外的全局3D参考信息进一步优化参数并将其对齐至地理坐标系。在线阶段，将任意新视角的标定问题建模为重定位任务，平衡了精度与计算效率以满足真实场景需求。大量实验表明，该方法在多种真实与合成数据集上均优于现有最先进方法。代码与数据集可通过https://github.com/gjgjh/PTZ-Calib获取。
原文摘要 · Abstract (English)
In this paper, we present PTZ-Calib, a robust two-stage PTZ camera calibration method, that efficiently and accurately estimates camera parameters for arbitrary viewpoints. Our method includes an offline and an online stage. In the offline stage, we first uniformly select a set of reference images that sufficiently overlap to encompass a complete 360° view. We then utilize the novel PTZ-IBA (PTZ Incremental Bundle Adjustment) algorithm to automatically calibrate the cameras within a local coordinate system. Additionally, for practical application, we can further optimize camera parameters and align them with the geographic coordinate system using extra global reference 3D information. In the online stage, we formulate the calibration of any new viewpoints as a relocalization problem. Our approach balances the accuracy and computational efficiency to meet real-world demands. Extensive evaluations demonstrate our robustness and superior performance over state-of-the-art methods on various real and synthetic datasets. Datasets and source code can be accessed online at https://github.com/gjgjh/PTZ-Calib
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