Cross-Parameterization for Triangular Meshes with Semantic Features

Abstract

In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings(DGP) such as morphing, shape blending, texture transfer, re-meshing and so on. This paper proposes a novel approach for parameterizing a mesh into another one directly. The main idea of our method is to combine a competitive learning and a leastsquare mesh techniques. It is enough to give some semantic feature correspondences between target meshes, even if they are in different shapes or in different poses. We show the effectiveness of our approach by giving some examples of its applications.