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

Nicolas Donati, Etienne Corman, Maks Ovsjanikov; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 742-751

Abstract


State-of-the-art fully intrinsic network for non-rigid shape matching are unable to disambiguate between shape inner symmetries. 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 DiffusionNet, which makes our method robust to discretization changes, while adding a vector-field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors. Our source code is available at: https://github.com/nicolasdonati/DUO-FM

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Donati_2022_CVPR, author = {Donati, Nicolas and Corman, Etienne and Ovsjanikov, Maks}, title = {Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {742-751} }