Three-dimensional surface registration: A neural network strategy

Three-dimensional surface registration is a necessary step and widely used in shape analysis, surface representation, and medical image-aided surgery. Traditional methods to fulfill such task are extremely computation complex and sometimes will obtain bad results if configured with unstructured mass data. In this paper, we propose a novel neural network strategy for efficient surface registration. Before surface registration, we use mesh PCA to normalize 3D model coordinate directions. The results and comparisons show that such neural network method is a promising approach for 3D surface registration.