Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院
Journal:CAD Comput Aided Des
Abstract:This paper presents a simple yet efficient framework for finding a set of sparse correspondences between two non-rigid shapes using a tensor-based optimization technique. To make the matching consistent, we propose to use third-order potentials to define the similarity tensor measure between triplets of feature points. Given two non-rigid 3D models, we first extract two sets of feature points residing in shape extremities, and then build the similarity tensor as a combination of the geodesic-based and prior-based similarities. The hyper-graph matching problem is formulated as the maximization of an objective function over all possible permutations of points, and it is solved by a tensor power iteration technique, which involves row/column normalization. Finally, a consistent set of discrete correspondences is automatically obtained. Various experimental results have demonstrated the superiority of our proposed method, compared with several state-of-the-art methods. © 2018 Elsevier Ltd
ISSN No.:0010-4485
Translation or Not:no
Date of Publication:2019-02-01
Co-author:Remil, Oussama,wuqiaoyun,Guo, Yanwen,Wang, Jun,Jun Wang
Correspondence Author:Remil, Oussama,xq