Name
Abstract | ||
|---|---|---|
In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps. The functional maps paradigm brings with it a number of advantages for face correspondence. First, it allows us to combine various notions of correspondence. We do so by proposing a number of face-specific functions, suited to either within-or between-subject correspondence. Second, we propose a groupwise variant of the method allowing us to compute cycle-consistent functional maps between all faces in a training set. Since functional maps are of much lower dimension than point-to-point correspondences, this is feasible even when the input meshes are very high resolution. Finally, we show how a functional map provides a geometric constraint that can be used to filter feature matches between non-rigidly deforming surfaces. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.544 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Training set,Computer vision,Functional Map,Polygon mesh,Pattern recognition,Computer science,Artificial intelligence | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Zhang | 1 | 939 | 103.66 |
| William A. P. Smith | 2 | 476 | 47.63 |
| Arnaud Dessein | 3 | 48 | 4.80 |
| Nick Pears | 4 | 410 | 30.57 |
| Hang Dai | 5 | 1 | 2.38 |