Multiple-view shape extraction from shading as local regression by analytic NN scheme

Abstract

We introduce a multiple-view 3D-shape-reconstruction system. This system is able to fuse few-view and erroneous depth maps into a more complete and more accurate shape representation using a unique neural network (NN). The NN provides analytic mapping and learning of a polyhedron model to approximate the true shape of an object based on multiple-view depth maps. The depth maps are obtained by a widely used Tsai-Shah shape-from-shading (SFS) algorithm. They are considered as partial 3D shapes of the object to be reconstructed. The main insight of this work is that the NN minimizes the depth map error in one view using depth maps information from other views observed under nonfixed light source positions relative to the object. Theoretically, we formulate our problem as nonparametric (local) regression in depth space formed by multiple view observations. Experimentally, we obtain exact and stable results through hierarchical reconstruction and annealing reinforcement. We provide the implementation of the NN used in this paper at .