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Unsupervised cycle‐consistent deformation for shape matching
Author(s) -
Groueix Thibault,
Fisher Matthew,
Kim Vladimir G.,
Russell Bryan C.,
Aubry Mathieu
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13794
Subject(s) - margin (machine learning) , segmentation , computer science , artificial intelligence , consistency (knowledge bases) , matching (statistics) , deformation (meteorology) , parametric statistics , point (geometry) , pattern recognition (psychology) , transformation (genetics) , constraint (computer aided design) , mathematics , machine learning , geometry , biochemistry , statistics , physics , chemistry , meteorology , gene
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.