{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HOKGAE32UGIM73XPSPRZTOC3CV","short_pith_number":"pith:HOKGAE32","schema_version":"1.0","canonical_sha256":"3b9460137aa190cfeeef93e399b85b154c7e464183fce5919271cac0d47f6a99","source":{"kind":"arxiv","id":"2104.09886","version":1},"attestation_state":"computed","paper":{"title":"Lighting, Reflectance and Geometry Estimation from 360$^{\\circ}$ Panoramic Stereo","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongdong Li, Junxuan Li, Yasuyuki Matsushita","submitted_at":"2021-04-20T10:41:50Z","abstract_excerpt":"We propose a method for estimating high-definition spatially-varying lighting, reflectance, and geometry of a scene from 360$^{\\circ}$ stereo images. Our model takes advantage of the 360$^{\\circ}$ input to observe the entire scene with geometric detail, then jointly estimates the scene's properties with physical constraints. We first reconstruct a near-field environment light for predicting the lighting at any 3D location within the scene. Then we present a deep learning model that leverages the stereo information to infer the reflectance and surface normal. Lastly, we incorporate the physical"},"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":"2104.09886","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-04-20T10:41:50Z","cross_cats_sorted":[],"title_canon_sha256":"595342cd6720a792e2149215f5aed305671e127965b5917300f2779be5b03752","abstract_canon_sha256":"4aa7a5c814ba030213b31b807342cbc3a5ee772855e6b01f533270fb56b34e53"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:33:38.116330Z","signature_b64":"Ape/8AmwPxUhtddsh4+orqv6hLzWoQE7Z/6p38/lYuvz0od0Xgv4GLdokj7p554rBdnORdNRXqfB5rxfGOQhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b9460137aa190cfeeef93e399b85b154c7e464183fce5919271cac0d47f6a99","last_reissued_at":"2026-07-05T02:33:38.115930Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:33:38.115930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lighting, Reflectance and Geometry Estimation from 360$^{\\circ}$ Panoramic Stereo","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongdong Li, Junxuan Li, Yasuyuki Matsushita","submitted_at":"2021-04-20T10:41:50Z","abstract_excerpt":"We propose a method for estimating high-definition spatially-varying lighting, reflectance, and geometry of a scene from 360$^{\\circ}$ stereo images. Our model takes advantage of the 360$^{\\circ}$ input to observe the entire scene with geometric detail, then jointly estimates the scene's properties with physical constraints. We first reconstruct a near-field environment light for predicting the lighting at any 3D location within the scene. Then we present a deep learning model that leverages the stereo information to infer the reflectance and surface normal. Lastly, we incorporate the physical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.09886","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/2104.09886/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":"2104.09886","created_at":"2026-07-05T02:33:38.115983+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.09886v1","created_at":"2026-07-05T02:33:38.115983+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.09886","created_at":"2026-07-05T02:33:38.115983+00:00"},{"alias_kind":"pith_short_12","alias_value":"HOKGAE32UGIM","created_at":"2026-07-05T02:33:38.115983+00:00"},{"alias_kind":"pith_short_16","alias_value":"HOKGAE32UGIM73XP","created_at":"2026-07-05T02:33:38.115983+00:00"},{"alias_kind":"pith_short_8","alias_value":"HOKGAE32","created_at":"2026-07-05T02:33:38.115983+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/HOKGAE32UGIM73XPSPRZTOC3CV","json":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV.json","graph_json":"https://pith.science/api/pith-number/HOKGAE32UGIM73XPSPRZTOC3CV/graph.json","events_json":"https://pith.science/api/pith-number/HOKGAE32UGIM73XPSPRZTOC3CV/events.json","paper":"https://pith.science/paper/HOKGAE32"},"agent_actions":{"view_html":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV","download_json":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV.json","view_paper":"https://pith.science/paper/HOKGAE32","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.09886&json=true","fetch_graph":"https://pith.science/api/pith-number/HOKGAE32UGIM73XPSPRZTOC3CV/graph.json","fetch_events":"https://pith.science/api/pith-number/HOKGAE32UGIM73XPSPRZTOC3CV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV/action/storage_attestation","attest_author":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV/action/author_attestation","sign_citation":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV/action/citation_signature","submit_replication":"https://pith.science/pith/HOKGAE32UGIM73XPSPRZTOC3CV/action/replication_record"}},"created_at":"2026-07-05T02:33:38.115983+00:00","updated_at":"2026-07-05T02:33:38.115983+00:00"}