Assessment of multi-camera calibration algorithms for two-dimensional camera arrays relative to ground truth position and direction
Assessment of multi-camera calibration algorithms for two-dimensional camera arrays relative to ground truth position and direction
复制标题
相对于地面真实位置和方向的二维相机阵列的多相机校准算法的评估
DOI：
10.1109/3dtv.2016.7548887
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
R. Olsson
中科院分区：
文献类型：
--
作者：
Elijs Dima;Mårten Sjöström;R. Olsson
相机校准方法通常是在完全不同的一维数据集上根据累积重新投影误差度量进行评估的。为了评估相机在二维Ar射线中的定标，需要在具有相机参数约束的二维数据集上进行评估。在这项研究中，对几种多摄像机标定方法的准确性进行了评估，其中摄像机参数对视角投影的影响最大。作为输入数据，我们使用了一个包含15个视点的二维数据集，该数据集具有内在和外在参数约束以及外在基本事实。评估结果表明，基于运动结构的自标定方法达到了与标准棋盘标定算法同等的内外参数估计精度，并超过了著名的自标定工具箱BlueCCal。这些结果表明，自校准是一种可行的二维摄像机阵列校准方法，但需要改进最新的多摄像机特征匹配，以使BlueCCal与其他二维摄像机阵列自校准方法一样准确。
Camera calibration methods are commonly evaluated on cumulative reprojection error metrics, on disparate one-dimensional datasets. To evaluate calibration of cameras in two-dimensional ar-rays, assessments need to be made on two-dimensional datasets with constraints on camera parameters. In this study, accuracy of several multi-camera calibration methods has been evaluated on camera parameters that are affecting view projection the most. As input data, we used a 15-viewpoint two-dimensional dataset with intrinsic and extrinsic parameter constraints and extrinsic ground truth. The assessment showed that self-calibration methods using structure-from-motion reach equal intrinsic and extrinsic parameter estimation accuracy with standard checkerboard calibration algorithm, and surpass a well-known self-calibration toolbox, BlueCCal. These results show that self-calibration is a viable approach to calibrating two-dimensional camera arrays, but improvements to state-of-art multi-camera feature matching are necessary to make BlueCCal as accurate as other self-calibration methods for two-dimensional camera arrays.
DOI：
10.1109/34.888718
发表时间：
2000-11-01
影响因子：
23.6
作者：
Zhang, ZY
通讯作者：
Zhang, ZY