Unsupervised Cycle-consistent Deformation for Shape Matching
Loading...
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}
}