  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
https://github.com/Not-IITian/Weakly-supervised-Functional-map
