Intrinsic shape matching via tensor-based optimization
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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：CAD Comput Aided Des
摘要：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号：0010-4485
是否译文：否
发表时间：2019-02-01
合写作者：Remil, Oussama,吴巧云,Guo, Yanwen,Wang, Jun,汪俊
通讯作者：Remil, Oussama,谢强