A Convolutional Neural Network for Nonrigid Structure from Motion

Abstract

In this study, we propose a reconstruction and optimization neural network (RONN), a novel neural network for nonrigid structure from motion, which is completed by an unsupervised convolution neural network. Compared with the traditional method for directly solving 3D structures, our model focuses on depth information that is lost owing to projection. This mathematical model is developed using a convolutional neural network with three modules for integration, reconstruction, and optimization, as well as two prior-free loss functions. The proposed RONN achieves competitive accuracy on several tested sequences and high visual quality of various real video sequences.