Methods for obtaining normal vector, geometry and material of three-dimensional objects based on neural network

Abstract

A method for obtaining a normal vector, a geometry and a material of a three-dimensional object based on a neural network is provided. The present disclosure provides, based on an idea of 'actively irradiating an object with a number of specific patterns, capturing photos at the same time, and obtaining a normal vector of an object by calculating the obtained photos', an acquisition method combined with a neural network. Further, the method uses the obtained normal vector to optimize a model of the object. This method can also obtain material feature information while obtaining the normal vector. Finally, a high-quality geometric result and a high-quality material acquisition result are obtained jointly. The number of illumination patterns obtained by using this method is small, and the normal vector obtained by the method has high accuracy and the method is not limited to a specific acquisition device.