{"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/image2mesh-a-learning-framework-for-single","title":"Image2Mesh: A Learning Framework for Single Image 3D Reconstruction","arxiv_id":"1711.10669","date":"2017-11-29","proceeding":null,"authors":["Jhony K. Pontes","Chen Kong","Sridha Sridharan","Simon Lucey","Anders Eriksson","Clinton Fookes"],"abstract":"One challenge that remains open in 3D deep learning is how to efficiently\nrepresent 3D data to feed deep networks. Recent works have relied on volumetric\nor point cloud representations, but such approaches suffer from a number of\nissues such as computational complexity, unordered data, and lack of finer\ngeometry. This paper demonstrates that a mesh representation (i.e. vertices and\nfaces to form polygonal surfaces) is able to capture fine-grained geometry for\n3D reconstruction tasks. A mesh however is also unstructured data similar to\npoint clouds. We address this problem by proposing a learning framework to\ninfer the parameters of a compact mesh representation rather than learning from\nthe mesh itself. This compact representation encodes a mesh using free-form\ndeformation and a sparse linear combination of models allowing us to\nreconstruct 3D meshes from single images. In contrast to prior work, we do not\nrely on silhouettes and landmarks to perform 3D reconstruction. We evaluate our\nmethod on synthetic and real-world datasets with very promising results. Our\nframework efficiently reconstructs 3D objects in a low-dimensional way while\npreserving its important geometrical aspects.","url_abs":"http://arxiv.org/abs/1711.10669v1","url_pdf":"http://arxiv.org/pdf/1711.10669v1.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":"image2mesh-a-learning-framework-for-single","repo_url":"https://github.com/jhonykaesemodel/image2mesh","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.10669","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}