{"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/self-supervised-learning-of-dense-shape","title":"Self-supervised Learning of Dense Shape Correspondence","arxiv_id":"1812.02415","date":"2018-12-06","proceeding":null,"authors":["Oshri Halimi","Or Litany","Emanuele Rodolà","Alex Bronstein","Ron Kimmel"],"abstract":"We introduce the first completely unsupervised correspondence learning\napproach for deformable 3D shapes. Key to our model is the understanding that\nnatural deformations (such as changes in pose) approximately preserve the\nmetric structure of the surface, yielding a natural criterion to drive the\nlearning process toward distortion-minimizing predictions. On this basis, we\novercome the need for annotated data and replace it by a purely geometric\ncriterion. The resulting learning model is class-agnostic, and is able to\nleverage any type of deformable geometric data for the training phase. In\ncontrast to existing supervised approaches which specialize on the class seen\nat training time, we demonstrate stronger generalization as well as\napplicability to a variety of challenging settings. We showcase our method on a\nwide selection of correspondence benchmarks, where we outperform other methods\nin terms of accuracy, generalization, and efficiency.","url_abs":"http://arxiv.org/abs/1812.02415v1","url_pdf":"http://arxiv.org/pdf/1812.02415v1.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":"self-supervised-learning-of-dense-shape","repo_url":"https://github.com/OshriHalimi/unsupervised_learning_of_dense_shape_correspondence","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.02415","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}