A new robust algorithmic for multi-camera calibration with a 1D object under general motions without prior knowledge of any camera intrinsic parameter

Global calibration of non-overlapping cameras: State of the art

“…RMS error of this method is within 0.060 mm. Franca et al [17] also used 1D target for global calibration. The cameras are divided into binocular sets.…”

An adaptively weighted algorithm for camera calibration with 1D objects

“…Next, we analyze the data errors of the constraints in the NC (15). Because normalized datax satisfies…”

“…Considering the data tðw i Þ in the NC, we have For our weighed constraints, if k ¼0, they simply become the NC in (15). In this paper, these constraints (23) are called the weighted normalized constraints (WNC).…”

“…This process is usually a necessary step in extracting 3D metric information from 2D images in many computer vision applications [1][2][3]. Depending on the dimensions of calibration objects, camera calibration techniques can be roughly classified into four categories: 3D calibration [4], 2D calibration [5], 1D calibration [6][7][8][9][10][11][12][13][14][15][16] and 0D calibration or self-calibration [17,18,1]. In self-calibration, only correspondences between image points are used, without any calibration object.…”

“…The accuracy of 1D calibration still needs to be improved [13][14][15] because it is currently much lower than that of 2D or 3D calibration. There are many reasons for the poor accuracy of classical 1D calibration.…”