{"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-shape-abstractions-by-assembling","title":"Learning Shape Abstractions by Assembling Volumetric Primitives","arxiv_id":"1612.00404","date":"2016-12-01","proceeding":"CVPR 2017 7","authors":["Shubham Tulsiani","Hao Su","Leonidas J. Guibas","Alexei A. Efros","Jitendra Malik"],"abstract":"We present a learning framework for abstracting complex shapes by learning to\nassemble objects using 3D volumetric primitives. In addition to generating\nsimple and geometrically interpretable explanations of 3D objects, our\nframework also allows us to automatically discover and exploit consistent\nstructure in the data. We demonstrate that using our method allows predicting\nshape representations which can be leveraged for obtaining a consistent parsing\nacross the instances of a shape collection and constructing an interpretable\nshape similarity measure. We also examine applications for image-based\nprediction as well as shape manipulation.","url_abs":"http://arxiv.org/abs/1612.00404v4","url_pdf":"http://arxiv.org/pdf/1612.00404v4.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-shape-abstractions-by-assembling","repo_url":"https://github.com/shubhtuls/volumetricPrimitives","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"learning-shape-abstractions-by-assembling","repo_url":"https://github.com/hailieqh/3d-object-primitive-graph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-shape-abstractions-by-assembling","repo_url":"https://github.com/paschalidoud/superquadric_parsing","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"learning-shape-abstractions-by-assembling","repo_url":"https://github.com/nileshkulkarni/volumetricPrimitivesPytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.00404","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}