  Alignment between non-rigid stretchable structures is one of the hardest
tasks in computer vision, as the invariant properties are hard to define on one
hand, and on the other hand no labelled data exists for real datasets. We
present unsupervised neural network architecture based upon the spectrum of
scale-invariant geometry. We build ontop the functional maps architecture, but
show that learning local features, as done until now, is not enough once the
isometric assumption breaks but can be solved using scale-invariant geometry.
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.
