{"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/dula-net-a-dual-projection-network-for","title":"DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama","arxiv_id":"1811.11977","date":"2018-11-29","proceeding":"CVPR 2019 6","authors":["Shang-Ta Yang","Fu-En Wang","Chi-Han Peng","Peter Wonka","Min Sun","Hung-Kuo Chu"],"abstract":"We present a deep learning framework, called DuLa-Net, to predict\nManhattan-world 3D room layouts from a single RGB panorama. To achieve better\nprediction accuracy, our method leverages two projections of the panorama at\nonce, namely the equirectangular panorama-view and the perspective\nceiling-view, that each contains different clues about the room layouts. Our\nnetwork architecture consists of two encoder-decoder branches for analyzing\neach of the two views. In addition, a novel feature fusion structure is\nproposed to connect the two branches, which are then jointly trained to predict\nthe 2D floor plans and layout heights. To learn more complex room layouts, we\nintroduce the Realtor360 dataset that contains panoramas of Manhattan-world\nroom layouts with different numbers of corners. Experimental results show that\nour work outperforms recent state-of-the-art in prediction accuracy and\nperformance, especially in the rooms with non-cuboid layouts.","url_abs":"http://arxiv.org/abs/1811.11977v2","url_pdf":"http://arxiv.org/pdf/1811.11977v2.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":"dula-net-a-dual-projection-network-for","repo_url":"https://github.com/SunDaDenny/DuLa-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-room-layouts-from-a-single-rgb-panorama","task_name":"3D Room Layouts From A Single RGB Panorama"},{"task_slug":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-room-layouts-from-a-single-rgb-panorama-on","task":"3D Room Layouts From A Single RGB Panorama","dataset":"PanoContext","model":"DuLa-Net","rank_in_archive_order":6,"of":7,"metrics":{"3DIoU":"77.42%"},"uses_additional_data":false},{"leaderboard":"/sota/3d-room-layouts-from-a-single-rgb-panorama-on-2","task":"3D Room Layouts From A Single RGB Panorama","dataset":"Realtor360","model":"DuLa-Net","rank_in_archive_order":1,"of":2,"metrics":{"3DIoU":"77.2%"},"uses_additional_data":false},{"leaderboard":"/sota/3d-room-layouts-from-a-single-rgb-panorama-on-3","task":"3D Room Layouts From A Single RGB Panorama","dataset":"Stanford2D3D Panoramic","model":"DuLa-Net","rank_in_archive_order":8,"of":9,"metrics":{"3DIoU":"79.36"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.11977","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11977"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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