Multi-view stereo reconstruction of volumetric human face via graph-cuts and dynamic patch structure
基于图割及动态片结构的3维人脸多视图体重建

In this paper, a novel approach is proposed to reconstruct human face model from multi-view images with volumetric scene representation. This method focuses on improving the reconstruction results without the limit of the representative ability of 3D facial model samples. Our main contribution is to combine feature points based reconstruction and volumetric based reconstruction into the framework of Graph Cuts when applying facial reconstruction. Two considerations contribute to the improvement of final results. First, a dynamic patch based photo consistency estimation is designed to get the value of the photo-consistency constrain. Second, a dynamic weighted graph is constructed in order to avoid half voxel size error. We evaluate the performance on a real dataset, in which multi-view face images (8, 16, about 30) are captured by moving digital camera under changed illumination. The results indicate our approach have significant improvements.