{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JOB54TZH5OIXOWB4A2CIF2RS7R","short_pith_number":"pith:JOB54TZH","schema_version":"1.0","canonical_sha256":"4b83de4f27eb9177583c068482ea32fc5dd7c1dba71c44ad29cdd65071828ca5","source":{"kind":"arxiv","id":"2304.08580","version":1},"attestation_state":"computed","paper":{"title":"U2RLE: Uncertainty-Guided 2-Stage Room Layout Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Ivaylo Boyadzhiev, Jana Kosecka, Pooya Fayyazsanavi, Sing Bing Kang, Will Hutchcroft, Yuguang Li, Zhiqiang Wan","submitted_at":"2023-04-17T19:43:08Z","abstract_excerpt":"While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary. To tackle this problem, we propose a novel uncertainty-guided approach for layout boundary estimation introducing new two-stage CNN architecture termed U2RLE. The initial stage predicts both floor-wall boundary and its uncertainty and is followed by the refinement of boundaries with high positional uncertainty using a different, distance-aware loss. Finally, outputs from the two stages are merged to produce the room layout. Experim"},"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":"2304.08580","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-17T19:43:08Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"4f3930a43cb6029fc82ab05e7caf6d22a2a22ca6facfcb3734e04be7d77a0cfd","abstract_canon_sha256":"d5be37c9e840f15cefb702def3a28a039f32a16e94da1c09fa4ec7878a236a75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:02:11.366040Z","signature_b64":"4sDVZZLT1lblkOUe4nfvRfjg3hfinerG+xJzu9nDMyEod24+5rNY/T/W8/cha50htSwmFcW7G+0H9iARoyAfCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b83de4f27eb9177583c068482ea32fc5dd7c1dba71c44ad29cdd65071828ca5","last_reissued_at":"2026-07-05T06:02:11.365581Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:02:11.365581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"U2RLE: Uncertainty-Guided 2-Stage Room Layout Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Ivaylo Boyadzhiev, Jana Kosecka, Pooya Fayyazsanavi, Sing Bing Kang, Will Hutchcroft, Yuguang Li, Zhiqiang Wan","submitted_at":"2023-04-17T19:43:08Z","abstract_excerpt":"While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary. To tackle this problem, we propose a novel uncertainty-guided approach for layout boundary estimation introducing new two-stage CNN architecture termed U2RLE. The initial stage predicts both floor-wall boundary and its uncertainty and is followed by the refinement of boundaries with high positional uncertainty using a different, distance-aware loss. Finally, outputs from the two stages are merged to produce the room layout. Experim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.08580","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/2304.08580/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":"2304.08580","created_at":"2026-07-05T06:02:11.365638+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.08580v1","created_at":"2026-07-05T06:02:11.365638+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.08580","created_at":"2026-07-05T06:02:11.365638+00:00"},{"alias_kind":"pith_short_12","alias_value":"JOB54TZH5OIX","created_at":"2026-07-05T06:02:11.365638+00:00"},{"alias_kind":"pith_short_16","alias_value":"JOB54TZH5OIXOWB4","created_at":"2026-07-05T06:02:11.365638+00:00"},{"alias_kind":"pith_short_8","alias_value":"JOB54TZH","created_at":"2026-07-05T06:02:11.365638+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/JOB54TZH5OIXOWB4A2CIF2RS7R","json":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R.json","graph_json":"https://pith.science/api/pith-number/JOB54TZH5OIXOWB4A2CIF2RS7R/graph.json","events_json":"https://pith.science/api/pith-number/JOB54TZH5OIXOWB4A2CIF2RS7R/events.json","paper":"https://pith.science/paper/JOB54TZH"},"agent_actions":{"view_html":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R","download_json":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R.json","view_paper":"https://pith.science/paper/JOB54TZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.08580&json=true","fetch_graph":"https://pith.science/api/pith-number/JOB54TZH5OIXOWB4A2CIF2RS7R/graph.json","fetch_events":"https://pith.science/api/pith-number/JOB54TZH5OIXOWB4A2CIF2RS7R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R/action/storage_attestation","attest_author":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R/action/author_attestation","sign_citation":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R/action/citation_signature","submit_replication":"https://pith.science/pith/JOB54TZH5OIXOWB4A2CIF2RS7R/action/replication_record"}},"created_at":"2026-07-05T06:02:11.365638+00:00","updated_at":"2026-07-05T06:02:11.365638+00:00"}