Unsupervised Cycle-consistent Deformation for Shape Matching

Loading...
Thumbnail Image
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We propose a self-supervised approach to deep surface deformation. Given a pair of shapes, our algorithm directly predicts a parametric transformation from one shape to the other respecting correspondences. Our insight is to use cycle-consistency to define a notion of good correspondences in groups of objects and use it as a supervisory signal to train our network. Our method combines does not rely on a template, assume near isometric deformations or rely on point-correspondence supervision. We demonstrate the efficacy of our approach by using it to transfer segmentation across shapes. We show, on Shapenet, that our approach is competitive with comparable state-of-the-art methods when annotated training data is readily available, but outperforms them by a large margin in the few-shot segmentation scenario.
Description

        
@article{
10.1111:cgf.13794
, journal = {Computer Graphics Forum}, title = {{
Unsupervised Cycle-consistent Deformation for Shape Matching
}}, author = {
Groueix, Thibault
and
Fisher, Matthew
and
Kim, Vladimir G.
and
Russel, Bryan C.
and
Aubry, Mathieu
}, year = {
2019
}, publisher = {
The Eurographics Association and John Wiley & Sons Ltd.
}, ISSN = {
1467-8659
}, DOI = {
10.1111/cgf.13794
} }
Citation
Collections