{"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/3d-r2n2-a-unified-approach-for-single-and","title":"3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction","arxiv_id":"1604.00449","date":"2016-04-02","proceeding":null,"authors":["Christopher B. Choy","Danfei Xu","JunYoung Gwak","Kevin Chen","Silvio Savarese"],"abstract":"Inspired by the recent success of methods that employ shape priors to achieve\nrobust 3D reconstructions, we propose a novel recurrent neural network\narchitecture that we call the 3D Recurrent Reconstruction Neural Network\n(3D-R2N2). The network learns a mapping from images of objects to their\nunderlying 3D shapes from a large collection of synthetic data. Our network\ntakes in one or more images of an object instance from arbitrary viewpoints and\noutputs a reconstruction of the object in the form of a 3D occupancy grid.\nUnlike most of the previous works, our network does not require any image\nannotations or object class labels for training or testing. Our extensive\nexperimental analysis shows that our reconstruction framework i) outperforms\nthe state-of-the-art methods for single view reconstruction, and ii) enables\nthe 3D reconstruction of objects in situations when traditional SFM/SLAM\nmethods fail (because of lack of texture and/or wide baseline).","url_abs":"http://arxiv.org/abs/1604.00449v1","url_pdf":"http://arxiv.org/pdf/1604.00449v1.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":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/Amaranth819/3dr2n2-tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/JeremyFisher/deep_level_sets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/Radhika009/CMPE_295B_MASTERPROJECT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/Xharlie/ShapenetRender_more_variation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/chrischoy/3D-R2N2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/liuzhengzhe/ISS-Image-as-Stepping-Stone-for-Text-Guided-3D-Shape-Generation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/liuzhengzhe/dreamstone-iss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/natowi/3D-reconstruction-with-Neural-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/pranavbajoria93/3D_Reconstruction_3DR2N2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/raphaelsulzer/dsr-benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/raphaelsulzer/dsrv-data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/ttaa9/genren","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"3d-r2n2-a-unified-approach-for-single-and","repo_url":"https://github.com/bhiziroglu/3D-R2N2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-object-reconstruction","task_name":"3D Object Reconstruction"},{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-reconstruction","task_name":"Object Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-reconstruction-on-data3dr2n2","task":"3D Object Reconstruction","dataset":"Data3D−R2N2","model":"3D-R2N2","rank_in_archive_order":9,"of":15,"metrics":{"3DIoU":"0.56"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-reconstruction-on-data3dr2n2","task":"3D Object Reconstruction","dataset":"Data3D−R2N2","model":"3D-R2N2","rank_in_archive_order":14,"of":15,"metrics":{"Avg F1":"39.01"},"uses_additional_data":false},{"leaderboard":"/sota/3d-reconstruction-on-dtu","task":"3D 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