Self-supervised Learning of Dense Shape Correspondence

By Oshri Halimi, Or Litany, Emanuele Rodolà, A. Bronstein, Ron Kimmel. 2018

23 citations recorded by the source corpus.

We introduce the first completely unsupervised correspondence learning approach for deformable 3D shapes. Key to our model is the understanding that natural deformations (such as changes in pose) approximately preserve the metric structure of the surface, yielding a natural criterion to drive the learning process toward distortion-minimizing predictions. On this basis, we overcome the need for annotated data and replace it by a purely geometric criterion. The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase. In contrast to existing supervised approaches which specialize on the class seen at training time, we demonstrate stronger generalization as well as applicability to a variety of challenging settings. We showcase our method on a wide selection of correspondence benchmarks, where we outperform other methods in terms of accuracy, generalization, and efficiency.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

PRNet: Self-Supervised Learning for Partial-to-Partial Registration (2019, methodology): “Mathematical methods like functional maps can be made ‘deep’ using priors learned from data: Deep functional maps [43, 44] learn descriptors rather than designing them by hand.”