{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/room-layout-estimation/papers/ran/1","list_of":"/task/room-layout-estimation","task":"Room Layout Estimation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,12],"of":12,"counts":{"archive_papers_tagged":44,"with_a_code_link":20,"where_syntology_ran_a_sample":12,"not_listed_spam_title":0,"listed":44,"listed_where_code_ran":12,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":8,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":8,"listed_every_run_a_failure_of_syntologys_instrument":4,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/room-layout-estimation/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/from-semi-supervised-to-omni-supervised-room","slug":"from-semi-supervised-to-omni-supervised-room","title":"From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds","date":"2023-01-31","arxiv_id":"2301.13865","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/from-semi-supervised-to-omni-supervised-room#ran","syntology_url":"https://syntology.ai/paper/2301.13865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13865"}},"official":{"repos":["air-discover/omni-pq"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-room-layout-estimation-from-a-cubemap-of","slug":"3d-room-layout-estimation-from-a-cubemap-of","title":"3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform","date":"2022-07-19","arxiv_id":"2207.09291","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":5,"n_ran_checked":5,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/3d-room-layout-estimation-from-a-cubemap-of#ran","syntology_url":"https://syntology.ai/paper/2207.09291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.09291"}},"official":{"repos":["starrah/dmh-net"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/psmnet-position-aware-stereo-merging-network","slug":"psmnet-position-aware-stereo-merging-network","title":"PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation","date":"2022-03-30","arxiv_id":"2203.15965","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":6,"n_ran_checked":7,"n_instrument":5,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":17,"phrase":"12 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/psmnet-position-aware-stereo-merging-network#ran","syntology_url":"https://syntology.ai/paper/2203.15965","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15965"}},"official":{"repos":["zillow/psmnet-layout"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/lgt-net-indoor-panoramic-room-layout","slug":"lgt-net-indoor-panoramic-room-layout","title":"LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network","date":"2022-03-03","arxiv_id":"2203.01824","repositories_listed":1,"syntology":{"n":41,"n_ran":29,"n_constructed":5,"n_ran_checked":20,"n_instrument":9,"n_unverified":12,"n_honours":2,"n_violates":1,"n_no_contract":17,"n_pointer_only":0,"phrase":"29 ran (of which 5 constructed an object rather than computing a result; 20 with no instrument failure: 2 honoured, 1 violated, 17 with no contract checked; 9 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/lgt-net-indoor-panoramic-room-layout#ran","syntology_url":"https://syntology.ai/paper/2203.01824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01824"}},"official":{"repos":["zhigangjiang/LGT-Net"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":9,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/imvoxelnet-image-to-voxels-projection-for","slug":"imvoxelnet-image-to-voxels-projection-for","title":"ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection","date":"2021-06-02","arxiv_id":"2106.01178","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/imvoxelnet-image-to-voxels-projection-for#ran","syntology_url":"https://syntology.ai/paper/2106.01178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01178"}},"official":null}},{"url":"/paper/learning-to-reconstruct-3d-non-cuboid-room","slug":"learning-to-reconstruct-3d-non-cuboid-room","title":"Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image","date":"2021-04-16","arxiv_id":"2104.07986","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-to-reconstruct-3d-non-cuboid-room#ran","syntology_url":"https://syntology.ai/paper/2104.07986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07986"}},"official":{"repos":["CYang0515/NonCuboidRoom"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/led2-net-monocular-360-layout-estimation-via","slug":"led2-net-monocular-360-layout-estimation-via","title":"LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering","date":"2021-04-01","arxiv_id":"2104.00568","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/led2-net-monocular-360-layout-estimation-via#ran","syntology_url":"https://syntology.ai/paper/2104.00568","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00568"}},"official":{"repos":["fuenwang/LED2-Net"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/total3dunderstanding-joint-layout-object-pose","slug":"total3dunderstanding-joint-layout-object-pose","title":"Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image","date":"2020-02-27","arxiv_id":"2002.12212","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/total3dunderstanding-joint-layout-object-pose#ran","syntology_url":"https://syntology.ai/paper/2002.12212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.12212"}},"official":{"repos":["yinyunie/Total3DUnderstanding"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/structured3d-a-large-photo-realistic-dataset","slug":"structured3d-a-large-photo-realistic-dataset","title":"Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling","date":"2019-08-01","arxiv_id":"1908.00222","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/structured3d-a-large-photo-realistic-dataset#ran","syntology_url":"https://syntology.ai/paper/1908.00222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.00222"}},"official":{"repos":["bertjiazheng/Structured3D"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cooperative-holistic-scene-understanding","slug":"cooperative-holistic-scene-understanding","title":"Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation","date":"2018-10-31","arxiv_id":"1810.13049","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":15,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":1,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/cooperative-holistic-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/1810.13049","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.13049"}},"official":{"repos":["thusiyuan/cooperative_scene_parsing"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/holistic-3d-scene-parsing-and-reconstruction","slug":"holistic-3d-scene-parsing-and-reconstruction","title":"Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image","date":"2018-08-07","arxiv_id":"1808.02201","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/holistic-3d-scene-parsing-and-reconstruction#ran","syntology_url":"https://syntology.ai/paper/1808.02201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.02201"}},"official":null}},{"url":"/paper/roomnet-end-to-end-room-layout-estimation","slug":"roomnet-end-to-end-room-layout-estimation","title":"RoomNet: End-to-End Room Layout Estimation","date":"2017-03-18","arxiv_id":"1703.06241","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/roomnet-end-to-end-room-layout-estimation#ran","syntology_url":"https://syntology.ai/paper/1703.06241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1703.06241"}},"official":null}}],"record_sha256":"4ae788a35192abcdd11fb964d1715fe8dcd3607a9d88d24bf536b0130c422df2","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}