Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cad.2018.10.001 | Computer-Aided Design |
Keywords | Field | DocType |
Sparse correspondences,Similarity tensor,Tensor power iteration,Third-order potentials | Mathematical optimization,Normalization (statistics),Tensor,Permutation,Algorithm,Geodesic,Power iteration,Mathematics,Instrumental and intrinsic value,Maximization | Journal |
Volume | ISSN | Citations |
107 | 0010-4485 | 0 |
PageRank | References | Authors |
0.34 | 21 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oussama Remil | 1 | 14 | 2.98 |
| Qian Xie | 2 | 16 | 9.82 |
| Qiaoyun Wu | 3 | 3 | 1.14 |
| Yan-Wen Guo | 4 | 348 | 39.32 |
| Jun Wang | 5 | 372 | 47.52 |