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- W3036695062 abstract "A variety of deep functional maps have been proposed recently, from fully supervised to totally unsupervised, with a range of loss functions as well as different regularization terms. However, it is still not clear what are minimum ingredients of a deep functional map pipeline and whether such ingredients unify or generalize all recent work on deep functional maps. We show empirically minimum components for obtaining state of the art results with different loss functions, supervised as well as unsupervised. Furthermore, we propose a novel framework designed for both full-to-full as well as partial to full shape matching that achieves state of the art results on several benchmark datasets outperforming even the fully supervised methods by a significant margin. Our code is publicly available at this https URL" @default.
- W3036695062 created "2020-06-25" @default.
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- W3036695062 date "2020-12-07" @default.
- W3036695062 modified "2023-09-27" @default.
- W3036695062 title "Weakly Supervised Deep Functional Map for Shape Matching" @default.
- W3036695062 hasPublicationYear "2020" @default.
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