3D Triangular Mesh Parameterization with Semantic Features Based on Competitive Learning Methods

Shun MATSUI  Kota AOKI  Hiroshi NAGAHASHI  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E91-D   No.11   pp.2718-2726
Publication Date: 2008/11/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e91-d.11.2718
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Computer Graphics
Keyword: 
cross-parameterization,  deformable mesh models,  digital geometry processing,  

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Summary: 
In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings such as morphing, shape blending, texture mapping, re-meshing and so on. Most of the previous approaches made use of an identical primitive domain to parameterize a mesh model. In recent works of mesh parameterization, more flexible and attractive methods that can create direct mappings between two meshes have been reported. These mappings are called "cross-parameterization" and typically preserve semantic feature correspondences between target meshes. 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 least-square 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.