Deep Color-Normal Residual Networks for Geometry Refinement Extracting Color Consistency and Fine Geometry

Abstract

In recent years, texture mapping in 3D modeling has been remarkably improved for realistic rendering. However, small error in reconstructed 3D geometry makes serious error in texture mapping. To address the problem, most of prior methods are devoted to refinement of 3D geometry without visual clues. In this work, we refine 3D geometry based on color consistency and surface normal using a deep neural network. Our method optimizes the location of each vertex maximizing the quality of related textures.