  Alignment between non-rigid stretchable structures is one of the most
challenging tasks in computer vision, as the invariant properties are hard to
define, and there is no labeled data for real datasets. We present unsupervised
neural network architecture based upon the spectral domain of scale-invariant
geometry. We build on top of the functional maps architecture, but show that
learning local features, as done until now, is not enough once the isometry
assumption breaks. We demonstrate the use of multiple scale-invariant
geometries for solving this problem. Our method is agnostic to local-scale
deformations and shows superior performance for matching shapes from different
domains when compared to existing spectral state-of-the-art solutions.
