{"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/surfnet-generating-3d-shape-surfaces-using","title":"SurfNet: Generating 3D shape surfaces using deep residual networks","arxiv_id":"1703.04079","date":"2017-03-12","proceeding":"CVPR 2017 7","authors":["Ayan Sinha","Asim Unmesh","Qi-Xing Huang","Karthik Ramani"],"abstract":"3D shape models are naturally parameterized using vertices and faces, \\ie,\ncomposed of polygons forming a surface. However, current 3D learning paradigms\nfor predictive and generative tasks using convolutional neural networks focus\non a voxelized representation of the object. Lifting convolution operators from\nthe traditional 2D to 3D results in high computational overhead with little\nadditional benefit as most of the geometry information is contained on the\nsurface boundary. Here we study the problem of directly generating the 3D shape\nsurface of rigid and non-rigid shapes using deep convolutional neural networks.\nWe develop a procedure to create consistent `geometry images' representing the\nshape surface of a category of 3D objects. We then use this consistent\nrepresentation for category-specific shape surface generation from a parametric\nrepresentation or an image by developing novel extensions of deep residual\nnetworks for the task of geometry image generation. Our experiments indicate\nthat our network learns a meaningful representation of shape surfaces allowing\nit to interpolate between shape orientations and poses, invent new shape\nsurfaces and reconstruct 3D shape surfaces from previously unseen images.","url_abs":"http://arxiv.org/abs/1703.04079v1","url_pdf":"http://arxiv.org/pdf/1703.04079v1.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":"surfnet-generating-3d-shape-surfaces-using","repo_url":"https://github.com/sinhayan/surfnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-shape-generation","task_name":"3D Shape Generation"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.04079","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}