{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/weakly-supervised-deep-functional-maps-for","title":"Weakly Supervised Deep Functional Maps for Shape Matching","arxiv_id":null,"date":"2020-12-01","proceeding":"NeurIPS 2020 12","authors":["Abhishek Sharma","Maks Ovsjanikov"],"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 the 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. Our code is publicly available at \\url{https://github.com/Not-IITian/Weakly-supervised-Functional-map}","url_abs":"http://proceedings.neurips.cc/paper/2020/hash/dfb84a11f431c62436cfb760e30a34fe-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2020/file/dfb84a11f431c62436cfb760e30a34fe-Paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"weakly-supervised-deep-functional-maps-for","repo_url":"https://github.com/Not-IITian/Weakly-supervised-Functional-map","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}