{"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/topnet-structural-point-cloud-decoder","title":"TopNet: Structural Point Cloud Decoder","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Lyne P. Tchapmi"," Vineet Kosaraju"," Hamid Rezatofighi"," Ian Reid"," Silvio Savarese"],"abstract":"3D point cloud generation is of great use for 3D scene modeling and understanding.  Real-world 3D object point clouds  can  be  properly  described  by  a  collection  of  low-level and high-level structures such as surfaces, geometric primitives, semantic parts,etc. In fact, there exist many different representations of a 3D object point cloud as a set of point groups.  Existing frameworks for point cloud genera-ion either do not consider structure in their proposed solutions, or assume and enforce a specific structure/topology,e.g.  a  collection  of  manifolds  or  surfaces,  for  the  generated  point  cloud  of  a  3D  object.   In  this  work,  we  pro-pose a novel decoder that generates a structured point cloud without assuming any specific structure or topology on the underlying point set.  Our decoder is softly constrained to generate a point cloud following a hierarchical rooted tree structure.  We show that given enough capacity and allowing for redundancies, the proposed decoder is very flexible and able to learn any arbitrary grouping of points including any topology on the point set.  We evaluate our decoder on the task of point cloud generation for 3D point cloud shape completion.  Combined with encoders from existing frameworks, we show that our proposed decoder significantly outperforms state-of-the-art 3D point cloud completion methods on the Shapenet dataset\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_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":"topnet-structural-point-cloud-decoder","repo_url":"https://github.com/lynetcha/completion3d","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"object","task_name":"Object"},{"task_slug":"point-cloud-completion","task_name":"Point Cloud Completion"}],"methods":[],"datasets_introduced":[{"slug":"completion3d","name":"Completion3D","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/point-cloud-completion-on-completion3d","task":"Point Cloud Completion","dataset":"Completion3D","model":"TopNet","rank_in_archive_order":1,"of":7,"metrics":{"Chamfer Distance":"14.25(?)"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}