{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UWMA4AOF5VSPM27UBIQWRX3X2P","short_pith_number":"pith:UWMA4AOF","schema_version":"1.0","canonical_sha256":"a5980e01c5ed64f66bf40a2168df77d3c2c794766b2015f40b8b74482c2f7e25","source":{"kind":"arxiv","id":"2304.12294","version":2},"attestation_state":"computed","paper":{"title":"Explicit Correspondence Matching for Generalizable Neural Radiance Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanxia Zheng, Haofei Xu, Jianfei Cai, Qianyi Wu, Tat-Jen Cham, Yuedong Chen","submitted_at":"2023-04-24T17:46:01Z","abstract_excerpt":"We present a new generalizable NeRF method that is able to directly generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The key to our approach lies in the explicitly modeled correspondence matching information, so as to provide the geometry prior to the prediction of NeRF color and density for volume rendering. The explicit correspondence matching is quantified with the cosine similarity between image features sampled at the 2D projections of a 3D point on different views, which is able to provide reliable cues about the surface geometry. Unlik"},"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":"2304.12294","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-24T17:46:01Z","cross_cats_sorted":[],"title_canon_sha256":"c6fee2a8711539e9ab34efbeab69f3f45f6efaf83282086688413a3cf8f04e3b","abstract_canon_sha256":"48f92c49230698f7db8c6cf824031cfaf2e266f7a12ac262172cc6649dbe3ac0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:29.824179Z","signature_b64":"eAUPx4tDy7b3zDEBS240faReUCtHJGkaCltXs0ZT6rzBqVQGjyQ/QXJmNM5QBhY07O85qjEWjzXvsbOt46UKBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5980e01c5ed64f66bf40a2168df77d3c2c794766b2015f40b8b74482c2f7e25","last_reissued_at":"2026-07-05T11:57:29.823769Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:29.823769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explicit Correspondence Matching for Generalizable Neural Radiance Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanxia Zheng, Haofei Xu, Jianfei Cai, Qianyi Wu, Tat-Jen Cham, Yuedong Chen","submitted_at":"2023-04-24T17:46:01Z","abstract_excerpt":"We present a new generalizable NeRF method that is able to directly generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The key to our approach lies in the explicitly modeled correspondence matching information, so as to provide the geometry prior to the prediction of NeRF color and density for volume rendering. The explicit correspondence matching is quantified with the cosine similarity between image features sampled at the 2D projections of a 3D point on different views, which is able to provide reliable cues about the surface geometry. Unlik"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.12294","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/2304.12294/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":"2304.12294","created_at":"2026-07-05T11:57:29.823829+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.12294v2","created_at":"2026-07-05T11:57:29.823829+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.12294","created_at":"2026-07-05T11:57:29.823829+00:00"},{"alias_kind":"pith_short_12","alias_value":"UWMA4AOF5VSP","created_at":"2026-07-05T11:57:29.823829+00:00"},{"alias_kind":"pith_short_16","alias_value":"UWMA4AOF5VSPM27U","created_at":"2026-07-05T11:57:29.823829+00:00"},{"alias_kind":"pith_short_8","alias_value":"UWMA4AOF","created_at":"2026-07-05T11:57:29.823829+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09479","citing_title":"TinySplat: Feedforward Approach for Generating Compact 3D Scene Representation","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P","json":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P.json","graph_json":"https://pith.science/api/pith-number/UWMA4AOF5VSPM27UBIQWRX3X2P/graph.json","events_json":"https://pith.science/api/pith-number/UWMA4AOF5VSPM27UBIQWRX3X2P/events.json","paper":"https://pith.science/paper/UWMA4AOF"},"agent_actions":{"view_html":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P","download_json":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P.json","view_paper":"https://pith.science/paper/UWMA4AOF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.12294&json=true","fetch_graph":"https://pith.science/api/pith-number/UWMA4AOF5VSPM27UBIQWRX3X2P/graph.json","fetch_events":"https://pith.science/api/pith-number/UWMA4AOF5VSPM27UBIQWRX3X2P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P/action/storage_attestation","attest_author":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P/action/author_attestation","sign_citation":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P/action/citation_signature","submit_replication":"https://pith.science/pith/UWMA4AOF5VSPM27UBIQWRX3X2P/action/replication_record"}},"created_at":"2026-07-05T11:57:29.823829+00:00","updated_at":"2026-07-05T11:57:29.823829+00:00"}