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

Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching