{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:PWDPTB736XDBDXBM7HV5KMEDLR","short_pith_number":"pith:PWDPTB73","schema_version":"1.0","canonical_sha256":"7d86f987fbf5c611dc2cf9ebd530835c7c87471ff264d959071c06b72f077a53","source":{"kind":"arxiv","id":"2110.00644","version":1},"attestation_state":"computed","paper":{"title":"RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chun-Kai Wang, Himanshu Arora, Kenan Deng, Tomas Yago-Vicente, Xi Zhang","submitted_at":"2021-10-01T20:42:49Z","abstract_excerpt":"In this paper, we present a new approach to estimate the layout of a room from its single image. While recent approaches for this task use robust features learnt from data, they resort to optimization for detecting the final layout. In addition to using learnt robust features, our approach learns an additional ranking function to estimate the final layout instead of using optimization. To learn this ranking function, we propose a framework to train a CNN using max-margin structure cost. Also, while most approaches aim at detecting cuboidal layouts, our approach detects non-cuboidal layouts for"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2110.00644","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2021-10-01T20:42:49Z","cross_cats_sorted":[],"title_canon_sha256":"0f04f28489fa34e519644f9bcafd5fc2f7cfd4c5eea45b4b883f73166d786224","abstract_canon_sha256":"89ad3e5e29469c771a161a8ab3cc2f345238153b04ca8ffa6d7a22b5b2aff767"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:19:32.234551Z","signature_b64":"k9GFpK47fsMExTH/To0RRyEz21qBLkvt/JlljwEAl28u2HEqUg5yk9LABEtTeJYPTeQ58VWS+Wey6V2vSS4+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d86f987fbf5c611dc2cf9ebd530835c7c87471ff264d959071c06b72f077a53","last_reissued_at":"2026-07-05T03:19:32.234118Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:19:32.234118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chun-Kai Wang, Himanshu Arora, Kenan Deng, Tomas Yago-Vicente, Xi Zhang","submitted_at":"2021-10-01T20:42:49Z","abstract_excerpt":"In this paper, we present a new approach to estimate the layout of a room from its single image. While recent approaches for this task use robust features learnt from data, they resort to optimization for detecting the final layout. In addition to using learnt robust features, our approach learns an additional ranking function to estimate the final layout instead of using optimization. To learn this ranking function, we propose a framework to train a CNN using max-margin structure cost. Also, while most approaches aim at detecting cuboidal layouts, our approach detects non-cuboidal layouts for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.00644","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.00644/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2110.00644","created_at":"2026-07-05T03:19:32.234176+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.00644v1","created_at":"2026-07-05T03:19:32.234176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.00644","created_at":"2026-07-05T03:19:32.234176+00:00"},{"alias_kind":"pith_short_12","alias_value":"PWDPTB736XDB","created_at":"2026-07-05T03:19:32.234176+00:00"},{"alias_kind":"pith_short_16","alias_value":"PWDPTB736XDBDXBM","created_at":"2026-07-05T03:19:32.234176+00:00"},{"alias_kind":"pith_short_8","alias_value":"PWDPTB73","created_at":"2026-07-05T03:19:32.234176+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR","json":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR.json","graph_json":"https://pith.science/api/pith-number/PWDPTB736XDBDXBM7HV5KMEDLR/graph.json","events_json":"https://pith.science/api/pith-number/PWDPTB736XDBDXBM7HV5KMEDLR/events.json","paper":"https://pith.science/paper/PWDPTB73"},"agent_actions":{"view_html":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR","download_json":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR.json","view_paper":"https://pith.science/paper/PWDPTB73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.00644&json=true","fetch_graph":"https://pith.science/api/pith-number/PWDPTB736XDBDXBM7HV5KMEDLR/graph.json","fetch_events":"https://pith.science/api/pith-number/PWDPTB736XDBDXBM7HV5KMEDLR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR/action/storage_attestation","attest_author":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR/action/author_attestation","sign_citation":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR/action/citation_signature","submit_replication":"https://pith.science/pith/PWDPTB736XDBDXBM7HV5KMEDLR/action/replication_record"}},"created_at":"2026-07-05T03:19:32.234176+00:00","updated_at":"2026-07-05T03:19:32.234176+00:00"}