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Weakly Supervised Deep Functional Map for Shape Matching

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 all benchmark datasets outperforming even the fully supervised methods by a significant margin.
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Submitted on : Monday, September 28, 2020 - 3:06:50 AM
Last modification on : Wednesday, October 14, 2020 - 4:13:38 AM

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  • HAL Id : hal-02872053, version 2

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Abhishek Sharma, Maks Ovsjanikov. Weakly Supervised Deep Functional Map for Shape Matching. Neurips 2020, Dec 2020, Vancouver (Virtual Conference), Canada. ⟨hal-02872053v2⟩

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