Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction

This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network automatically combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors are applied to enforce integrability when reconstructing 3-D objects. Facial images and images of other general objects were used to test the proposed approach. The experimental results demonstrate that the proposed neural-network-based adaptive hybrid-reflectance model can be successfully applied to objects generally, and perform 3-D surface reconstruction better than some existing approaches.

I. Introduction

Shape recovery is a classical computer vision problem. The objective of shape recovery is to obtain a three-dimensional (3-D) scene description from one or more two-dimensional (2-D) images. The techniques used to recover the shape of an object are called shape-from-X techniques, where X denotes the specific information, such as shading, stereo, motion, and texture. Shape recovery from shading (SFS) is a major computer vision approach, which reconstructs the 3-D shape of an object from its gradual shading variation in 2-D images. When a point light source illuminates an object, they appear with different brightness, since the normal vectors corresponding to different parts of the object's surface are different. The spatial variation of brightness, referred to as shading, is used to estimate the orientation of surface and then calculate the depth map of the object. The recovered shape can be expressed in terms of depth, surface normal vector, surface gradient, or surface slant and tilt.