2020/09/28 by Abhishek Sharma, Maks Ovsjanikov, Sharma, Abhishek +1
Materials Science · Social Sciences · #Advanced Computing and Algorithms #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Graphics (cs.GR) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Supramolecular Self-Assembly in Materials
paper · pdf · doi:10.48550/arxiv.2009.13339
openalex publication_date 2020/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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