{"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/deepspline-data-driven-reconstruction-of","title":"DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces","arxiv_id":"1901.03781","date":"2019-01-12","proceeding":null,"authors":["Jun Gao","Chengcheng Tang","Vignesh Ganapathi-Subramanian","Jiahui Huang","Hao Su","Leonidas J. Guibas"],"abstract":"Reconstruction of geometry based on different input modes, such as images or\npoint clouds, has been instrumental in the development of computer aided design\nand computer graphics. Optimal implementations of these applications have\ntraditionally involved the use of spline-based representations at their core.\nMost such methods attempt to solve optimization problems that minimize an\noutput-target mismatch. However, these optimization techniques require an\ninitialization that is close enough, as they are local methods by nature. We\npropose a deep learning architecture that adapts to perform spline fitting\ntasks accordingly, providing complementary results to the aforementioned\ntraditional methods. We showcase the performance of our approach, by\nreconstructing spline curves and surfaces based on input images or point\nclouds.","url_abs":"http://arxiv.org/abs/1901.03781v1","url_pdf":"http://arxiv.org/pdf/1901.03781v1.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":"deepspline-data-driven-reconstruction-of","repo_url":"https://github.com/SteveJunGao/deepspline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"deepspline-data-driven-reconstruction-of","repo_url":"https://github.com/abrarum/bezierobjdet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.03781","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}