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 functional map








WeaklySupervisedDeepFunctionalMapforShape Matching

Neural Information Processing Systems

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 the minimum components for obtaining state-of-the-art results with different loss functions, supervised aswell asunsupervised.


Shaperegistrationinthetimeoftransformers

Neural Information Processing Systems

Alternatively,givenapair ofshapes, our method can register the first onto the second (or vice-versa), obtaining a high-quality dense correspondence betweenthetwo.