  We present a novel learning-based approach for computing correspondences
between non-rigid 3D shapes. Unlike previous methods that either require
extensive training data or operate on handcrafted input descriptors and thus
generalize poorly across diverse datasets, our approach is both accurate and
robust to changes in shape structure. Key to our method is a feature-extraction
network that learns directly from raw shape geometry, combined with a novel
regularized map extraction layer and loss, based on the functional map
representation. We demonstrate through extensive experiments in challenging
shape matching scenarios that our method can learn from less training data than
existing supervised approaches and generalizes significantly better than
current descriptor-based learning methods. Our source code is available at:
https://github.com/LIX-shape-analysis/GeomFmaps.
