Three-Dimensional Shape Reconstruction with Small Scale Based on Micro Stereo Vision

In this paper, three-dimensional data reconstruction with a small scale based on micro stereo-vision is discussed. An inversion model of stereo-vision that connects the object space and the image space is derived to obtain three-dimensional coordinates of object point in the object space from two-dimensional image coordinates of a pair of image points in the image space. An algorithm based on multi similarity measurements and multi-matching areas is proposed for stereo matching of color images, which considers the impacts of similarity measurements and matching areas fully. In this algorithm, the samples of matching output are acquired by the statistics of multi similarity measurements and multi-matching areas. On the other hand, the false matching results and the wrong matching results are partially filtered by performing the mean-square error statistics of matching output samples. Furthermore, a sub-pixel matching accuracy is expected by calculating the average values of two new samples derived from the mean-square error statistics. In the software platform based on VC++ and OpenGL, computer-aided design is designed for filter of abnormal data.