{"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/learning-descriptor-networks-for-3d-shape","title":"Learning Descriptor Networks for 3D Shape Synthesis and Analysis","arxiv_id":"1804.00586","date":"2018-04-02","proceeding":"CVPR 2018 6","authors":["Jianwen Xie","Zilong Zheng","Ruiqi Gao","Wenguan Wang","Song-Chun Zhu","Ying Nian Wu"],"abstract":"This paper proposes a 3D shape descriptor network, which is a deep\nconvolutional energy-based model, for modeling volumetric shape patterns. The\nmaximum likelihood training of the model follows an \"analysis by synthesis\"\nscheme and can be interpreted as a mode seeking and mode shifting process. The\nmodel can synthesize 3D shape patterns by sampling from the probability\ndistribution via MCMC such as Langevin dynamics. The model can be used to train\na 3D generator network via MCMC teaching. The conditional version of the 3D\nshape descriptor net can be used for 3D object recovery and 3D object\nsuper-resolution. Experiments demonstrate that the proposed model can generate\nrealistic 3D shape patterns and can be useful for 3D shape analysis.","url_abs":"http://arxiv.org/abs/1804.00586v1","url_pdf":"http://arxiv.org/pdf/1804.00586v1.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":"learning-descriptor-networks-for-3d-shape","repo_url":"https://github.com/jianwen-xie/3DDescriptorNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-object-super-resolution","task_name":"3D Object Super-Resolution"},{"task_slug":"object","task_name":"Object"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.00586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00586"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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