  State-of-the-art fully intrinsic networks for non-rigid shape matching often
struggle to disambiguate the symmetries of the shapes leading to unstable
correspondence predictions. Meanwhile, recent advances in the functional map
framework allow to enforce orientation preservation using a functional
representation for tangent vector field transfer, through so-called complex
functional maps. Using this representation, we propose a new deep learning
approach to learn orientation-aware features in a fully unsupervised setting.
Our architecture is built on top of DiffusionNet, making it robust to
discretization changes. Additionally, we introduce a vector field-based loss,
which promotes orientation preservation without using (often unstable)
extrinsic descriptors.
