Tensor Completion based 3D Reconstruction of Binocular Stereo Vision Binocular stereo vision is a common way for 3D reconstruction of objects, which is core technology in the fields of computeraided geometric design, machine vision and so on. However, the conventional binocular stereo vision algorithms are very sensitive to the ambient lighting, restricting their further promotion. The method of tensor completion, applied to the progress of preprocessing the captured binocular image, provides a way to solve this problem. In this paper, a binocular stereo vision algorithm that uses lowrank tensor completion to perform 3D reconstruction is presented. In the pictures, the positions where the ambient illumination are strong are regarded as information that cannot be observed, and the others are regarded as observable information. The unobservable information will be completed using the observable information based on tensor completion. The results show that the 3D reconstruction results obtained using tensor completion are better than those without tensor completion.

Modelassisted face reconstruction based on binocular stereo In this paper we propose a modelassisted binocular stereo algorithm for 3D face reconstruction. First, stereo correspondence is reliably performed between input stereo images by employing a 3D reference face model as a medium. Then, a highquality dataset of point cloud is generated by triangulation from the stereo correspondence. Finally, an accurate and featurepreserving 3D face surface is reconstructed from the point cloud dataset by a denoising operation of bilateral filtering and surface meshing. We have compared the reconstructed faces with ground truth 3D data. The experimental results show that the proposed algorithm is effective with the average error of 2.6mm.