Distortion Correction using Virtual PCG Pattern for Precise Stereo-based Large-scale 3D Measurement | IEEE Conference Publication | IEEE Xplore

Distortion Correction using Virtual PCG Pattern for Precise Stereo-based Large-scale 3D Measurement


Abstract:

Three-dimensional (3D) measurement is an essential procedure in various manufacturing industries including shipbuilding. Since binocular systems are convenient and time-s...Show More

Abstract:

Three-dimensional (3D) measurement is an essential procedure in various manufacturing industries including shipbuilding. Since binocular systems are convenient and time-saving, they are proposed for ship block measurement. However, because of the very large scale of the ship blocks, working distance is about 10 m, resulting in a fatal limitation of calibration: the size of image portion corresponding to the checkerboard for calibration included in the whole image is extremely small. This prevents the distortion parameter of camera lens which most affects 3D reconstruction accuracy, from being accurately estimated in the calibration. To overcome this limitation, this paper proposes a method that pre-estimates the distortion correction map that covers the entire image area. A phase-shift circular grating (PCG) pattern displayed on a monitor is captured by the camera set to large scale. Since PCG patterns are generated by computer software, infinite number of patterns corresponding to desired orientations and positions can be generated, which are useful to measure the center of distortion and more accurate vanishing points. Based on the estimated vanishing points, the accurate distortion correction is performed using perspective projection invariants, and the distortion values in pixels are measured for each grid points to estimate the distortion correction map of the entire image area. An experimental 3D measurement was conducted with an estimated distortion correction map. As a result, Mean and standard deviation of 3D reconstruction error by proposed method were improved by 15.84% and 6.77% compared with the Zhang’s method, respectively.
Date of Conference: 17-20 October 2022
Date Added to IEEE Xplore: 09 December 2022
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Conference Location: Brussels, Belgium

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