This paper outlines a new framework for the calibration of optical instruments, in particular smartphone cameras, using highly redundant circular black-and-white target fields. New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic eccentricity error of target centres; and (iii) iteratively improving the calibration solution through a free-network self-calibrating bundle adjustment. The proposed method effectively matched circular targets in 270 smartphone images, taken within a calibration laboratory, with robustness to type II errors (false negatives). The proposed eccentricity adjustment, which requires only camera projective matrices from two views, behaved comparably to available closed-form solutions, which require additional a priori object-space target information. Finally, specifically for the case of mobile devices, the calibration parameters obtained using the framework were found to be superior compared to in situ calibration for estimating the 3D reconstructed radius of a mechanical pipe (approximately 45% improvement on average).

中文翻译：

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用于机械管道 3D 重建的智能手机相机的自动校准

本文概述了使用高度冗余的圆形黑白目标场校准光学仪器（尤其是智能手机相机）的新框架。引入了新方法用于 (i) 图像之间的匹配目标；(ii) 调整目标中心的系统偏心误差；(iii) 通过自由网络自校准束调整迭代改进校准解决方案。所提出的方法有效地匹配了在校准实验室内拍摄的 270 个智能手机图像中的圆形目标，对 II 类错误（假阴性）具有鲁棒性。所提出的偏心率调整只需要来自两个视图的相机投影矩阵，其行为与可用的封闭形式解决方案相当，后者需要额外的先验对象空间目标信息。最后，用于估计机械管道的 3D 重建半径的原位校准（平均改进约 45 %）。