{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BQCZCIWL2NAGJJ2YC3OIA3PDUB","short_pith_number":"pith:BQCZCIWL","schema_version":"1.0","canonical_sha256":"0c059122cbd34064a75816dc806de3a048160a45c03fd8b0101c86a63d5ba8f4","source":{"kind":"arxiv","id":"2306.01531","version":2},"attestation_state":"computed","paper":{"title":"PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Chen Wang, Song-Hai Zhang, Yan-Pei Cao, Ying Shan, Yuan-Chen Guo, Zheng Chen","submitted_at":"2023-06-02T13:35:07Z","abstract_excerpt":"Achieving an immersive experience enabling users to explore virtual environments with six degrees of freedom (6DoF) is essential for various applications such as virtual reality (VR). Wide-baseline panoramas are commonly used in these applications to reduce network bandwidth and storage requirements. However, synthesizing novel views from these panoramas remains a key challenge. Although existing neural radiance field methods can produce photorealistic views under narrow-baseline and dense image captures, they tend to overfit the training views when dealing with \\emph{wide-baseline} panoramas "},"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":"2306.01531","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-02T13:35:07Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"befba922ad84874d597c0245031a9a55f2e1000d4aee6d409d0f5ea37405346f","abstract_canon_sha256":"fe46cb3c1957b6d83739a033ab7c1e9e7f92c330a6d41b97f987720d6183abd4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:51.640215Z","signature_b64":"LzI2wBzsQ0P5z/A9yMoVzn6B5DcVr9VLpUMVXyMBF2pnzCQKHR4dE5gzQNdVSbV2vbp9/xUdAoZzpKv18u4rDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c059122cbd34064a75816dc806de3a048160a45c03fd8b0101c86a63d5ba8f4","last_reissued_at":"2026-07-05T07:20:51.639739Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:51.639739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Chen Wang, Song-Hai Zhang, Yan-Pei Cao, Ying Shan, Yuan-Chen Guo, Zheng Chen","submitted_at":"2023-06-02T13:35:07Z","abstract_excerpt":"Achieving an immersive experience enabling users to explore virtual environments with six degrees of freedom (6DoF) is essential for various applications such as virtual reality (VR). Wide-baseline panoramas are commonly used in these applications to reduce network bandwidth and storage requirements. However, synthesizing novel views from these panoramas remains a key challenge. Although existing neural radiance field methods can produce photorealistic views under narrow-baseline and dense image captures, they tend to overfit the training views when dealing with \\emph{wide-baseline} panoramas "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.01531","kind":"arxiv","version":2},"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/2306.01531/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":"2306.01531","created_at":"2026-07-05T07:20:51.639798+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.01531v2","created_at":"2026-07-05T07:20:51.639798+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.01531","created_at":"2026-07-05T07:20:51.639798+00:00"},{"alias_kind":"pith_short_12","alias_value":"BQCZCIWL2NAG","created_at":"2026-07-05T07:20:51.639798+00:00"},{"alias_kind":"pith_short_16","alias_value":"BQCZCIWL2NAGJJ2Y","created_at":"2026-07-05T07:20:51.639798+00:00"},{"alias_kind":"pith_short_8","alias_value":"BQCZCIWL","created_at":"2026-07-05T07:20:51.639798+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30352","citing_title":"FastPano3D: Feed-Forward Indoor Panoramic 3D Reconstruction from a Single Image","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB","json":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB.json","graph_json":"https://pith.science/api/pith-number/BQCZCIWL2NAGJJ2YC3OIA3PDUB/graph.json","events_json":"https://pith.science/api/pith-number/BQCZCIWL2NAGJJ2YC3OIA3PDUB/events.json","paper":"https://pith.science/paper/BQCZCIWL"},"agent_actions":{"view_html":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB","download_json":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB.json","view_paper":"https://pith.science/paper/BQCZCIWL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.01531&json=true","fetch_graph":"https://pith.science/api/pith-number/BQCZCIWL2NAGJJ2YC3OIA3PDUB/graph.json","fetch_events":"https://pith.science/api/pith-number/BQCZCIWL2NAGJJ2YC3OIA3PDUB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB/action/storage_attestation","attest_author":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB/action/author_attestation","sign_citation":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB/action/citation_signature","submit_replication":"https://pith.science/pith/BQCZCIWL2NAGJJ2YC3OIA3PDUB/action/replication_record"}},"created_at":"2026-07-05T07:20:51.639798+00:00","updated_at":"2026-07-05T07:20:51.639798+00:00"}