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Journal Articles Computer Graphics Forum Year : 2019

Unsupervised cycle-consistent deformation for shape matching

Thibault Groueix
Vladimir G. Kim
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Bryan C. Russell
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Mathieu Aubry
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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 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.

Dates and versions

hal-02178969 , version 1 (10-07-2019)

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Cite

Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry. Unsupervised cycle-consistent deformation for shape matching. Computer Graphics Forum, 2019, 38 (5), pp.123-133. ⟨10.1111/cgf.13794⟩. ⟨hal-02178969⟩
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