{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OPSDY4WM6GECUGHSMFCPEOZT5R","short_pith_number":"pith:OPSDY4WM","schema_version":"1.0","canonical_sha256":"73e43c72ccf1882a18f26144f23b33ec70c9abaaa9b30ff1904cf1624e1d987a","source":{"kind":"arxiv","id":"2606.30047","version":1},"attestation_state":"computed","paper":{"title":"Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cihui Pan, Kai Zhang, Linyuan Li, Tong Rao, Xi Li, Xinchen Hui, Yan Wu","submitted_at":"2026-06-29T09:39:17Z","abstract_excerpt":"Metric feed-forward 3D reconstruction for panoramic data remains under-explored due to the lack of large-scale panoramic RGB-D training data.\n  We present Realsee3D, a hybrid dataset of 10K indoor scenes (1K real, 9K synthetic) with 299K panoramic viewpoints and precise metric annotations,\n  and Argus, a feed-forward network trained on it for metric panoramic 3D reconstruction.\n  In the sparse unordered capture setting of Realsee3D,\n  a poorly chosen coordinate anchor can cause global pose drift.\n  Argus addresses this with a learned covisibility module that selects the geometrically optimal r"},"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":"2606.30047","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-29T09:39:17Z","cross_cats_sorted":[],"title_canon_sha256":"8016fa407136831efdca933c01d42c6f8670ed0f486ff6c4a659378d0be1a8e6","abstract_canon_sha256":"61984a0ed500ce20d157b6d9ac4ce2e1da1f42dbdc0f9f9038fa0c1ac2ba3090"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:47.406281Z","signature_b64":"3pUh1naj7O9MF7pR4pV1QOXu1PJ80FCgXiAgIkOZeS8p4ipyL0tVUKKi7ZDXwXQBY8JiU3TuOsTKQepV3jZeAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73e43c72ccf1882a18f26144f23b33ec70c9abaaa9b30ff1904cf1624e1d987a","last_reissued_at":"2026-06-30T02:17:47.405772Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:47.405772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cihui Pan, Kai Zhang, Linyuan Li, Tong Rao, Xi Li, Xinchen Hui, Yan Wu","submitted_at":"2026-06-29T09:39:17Z","abstract_excerpt":"Metric feed-forward 3D reconstruction for panoramic data remains under-explored due to the lack of large-scale panoramic RGB-D training data.\n  We present Realsee3D, a hybrid dataset of 10K indoor scenes (1K real, 9K synthetic) with 299K panoramic viewpoints and precise metric annotations,\n  and Argus, a feed-forward network trained on it for metric panoramic 3D reconstruction.\n  In the sparse unordered capture setting of Realsee3D,\n  a poorly chosen coordinate anchor can cause global pose drift.\n  Argus addresses this with a learned covisibility module that selects the geometrically optimal r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30047","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/2606.30047/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":"2606.30047","created_at":"2026-06-30T02:17:47.405841+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30047v1","created_at":"2026-06-30T02:17:47.405841+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30047","created_at":"2026-06-30T02:17:47.405841+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPSDY4WM6GEC","created_at":"2026-06-30T02:17:47.405841+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPSDY4WM6GECUGHS","created_at":"2026-06-30T02:17:47.405841+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPSDY4WM","created_at":"2026-06-30T02:17:47.405841+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/OPSDY4WM6GECUGHSMFCPEOZT5R","json":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R.json","graph_json":"https://pith.science/api/pith-number/OPSDY4WM6GECUGHSMFCPEOZT5R/graph.json","events_json":"https://pith.science/api/pith-number/OPSDY4WM6GECUGHSMFCPEOZT5R/events.json","paper":"https://pith.science/paper/OPSDY4WM"},"agent_actions":{"view_html":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R","download_json":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R.json","view_paper":"https://pith.science/paper/OPSDY4WM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30047&json=true","fetch_graph":"https://pith.science/api/pith-number/OPSDY4WM6GECUGHSMFCPEOZT5R/graph.json","fetch_events":"https://pith.science/api/pith-number/OPSDY4WM6GECUGHSMFCPEOZT5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R/action/storage_attestation","attest_author":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R/action/author_attestation","sign_citation":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R/action/citation_signature","submit_replication":"https://pith.science/pith/OPSDY4WM6GECUGHSMFCPEOZT5R/action/replication_record"}},"created_at":"2026-06-30T02:17:47.405841+00:00","updated_at":"2026-06-30T02:17:47.405841+00:00"}