{"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/pyramid-u-network-for-skeleton-extraction","title":"Pyramid U-Network for Skeleton Extraction From Shape Points","arxiv_id":null,"date":"2019-06-17","proceeding":"IEEE 2019 CVPR Workshop 2019 6","authors":["Rowel Atienza"],"abstract":"The knowledge about the skeleton of a given geometric shape has many practical applications such as shape animation, shape comparison, shape recognition, and estimating structural strength. Skeleton extraction becomes a more challenging problem when the topology is represented in point cloud domain. In this paper, we present the network architecture, PSPU-SkelNet, for TeamPH which ranked 3rd in Point SkelNetOn 2019 challenge. PSPU-SkelNet is a pyramid of three U-Nets that predicts the skeleton from a given shape point cloud. PSPU-SkelNet achieves a Chamfer Distance (CD) of 2.9105 on the final test dataset. The code of PSPU SkelNet is available at https://github.com/roatienza/skelnet.","url_abs":"http://openaccess.thecvf.com/content_CVPRW_2019/papers/SkelNetOn/Atienza_Pyramid_U-Network_for_Skeleton_Extraction_From_Shape_Points_CVPRW_2019_paper.pdf","url_pdf":"http://openaccess.thecvf.com/content_CVPRW_2019/papers/SkelNetOn/Atienza_Pyramid_U-Network_for_Skeleton_Extraction_From_Shape_Points_CVPRW_2019_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":"pyramid-u-network-for-skeleton-extraction","repo_url":"https://github.com/roatienza/skelnet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"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}