{"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/multiview-compressive-coding-for-3d","title":"Multiview Compressive Coding for 3D Reconstruction","arxiv_id":"2301.08247","date":"2023-01-19","proceeding":"CVPR 2023 1","authors":["Chao-yuan Wu","Justin Johnson","Jitendra Malik","Christoph Feichtenhofer","Georgia Gkioxari"],"abstract":"A central goal of visual recognition is to understand objects and scenes from a single image. 2D recognition has witnessed tremendous progress thanks to large-scale learning and general-purpose representations. Comparatively, 3D poses new challenges stemming from occlusions not depicted in the image. Prior works try to overcome these by inferring from multiple views or rely on scarce CAD models and category-specific priors which hinder scaling to novel settings. In this work, we explore single-view 3D reconstruction by learning generalizable representations inspired by advances in self-supervised learning. We introduce a simple framework that operates on 3D points of single objects or whole scenes coupled with category-agnostic large-scale training from diverse RGB-D videos. Our model, Multiview Compressive Coding (MCC), learns to compress the input appearance and geometry to predict the 3D structure by querying a 3D-aware decoder. MCC's generality and efficiency allow it to learn from large-scale and diverse data sources with strong generalization to novel objects imagined by DALL$\\cdot$E 2 or captured in-the-wild with an iPhone.","url_abs":"https://arxiv.org/abs/2301.08247v1","url_pdf":"https://arxiv.org/pdf/2301.08247v1.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":"multiview-compressive-coding-for-3d","repo_url":"https://github.com/facebookresearch/mcc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"},{"task_slug":"single-view-3d-reconstruction","task_name":"Single-View 3D Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/single-view-3d-reconstruction-on-common","task":"Single-View 3D Reconstruction","dataset":"Common Objects in 3D","model":"MCC","rank_in_archive_order":2,"of":3,"metrics":{"Avg. 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