{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CLQPJS736JCCHGB5YPS3OANNQU","short_pith_number":"pith:CLQPJS73","schema_version":"1.0","canonical_sha256":"12e0f4cbfbf24423983dc3e5b701ad850ea749f7c3474f9ab637fe28f33c8d58","source":{"kind":"arxiv","id":"2105.07112","version":7},"attestation_state":"computed","paper":{"title":"NeuLF: Efficient Novel View Synthesis with Neural 4D Light Field","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Celong Liu, Junsong Yuan, Liangchen Song, Yi Xu, Zhong Li","submitted_at":"2021-05-15T01:20:30Z","abstract_excerpt":"In this paper, we present an efficient and robust deep learning solution for novel view synthesis of complex scenes. In our approach, a 3D scene is represented as a light field, i.e., a set of rays, each of which has a corresponding color when reaching the image plane. For efficient novel view rendering, we adopt a two-plane parameterization of the light field, where each ray is characterized by a 4D parameter. We then formulate the light field as a 4D function that maps 4D coordinates to corresponding color values. We train a deep fully connected network to optimize this implicit function and"},"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":"2105.07112","kind":"arxiv","version":7},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-15T01:20:30Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"40cd4e5bc73f468d6af12ae7d0811517b02ea97113899ae057657d5bfc4683e0","abstract_canon_sha256":"7c8427b425e813738abaa05130a551aa2091742f9c3b6ea074a33ae9675436c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:38:10.662247Z","signature_b64":"K0NRhAdCQcv+PaOxK4sBlZLPAQVbRHrmGmEenC6huqFg/35yPjh0l0QcLIDgocEc5vKYqpzBCDYuda5wh8m5Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12e0f4cbfbf24423983dc3e5b701ad850ea749f7c3474f9ab637fe28f33c8d58","last_reissued_at":"2026-07-05T04:38:10.661678Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:38:10.661678Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NeuLF: Efficient Novel View Synthesis with Neural 4D Light Field","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Celong Liu, Junsong Yuan, Liangchen Song, Yi Xu, Zhong Li","submitted_at":"2021-05-15T01:20:30Z","abstract_excerpt":"In this paper, we present an efficient and robust deep learning solution for novel view synthesis of complex scenes. In our approach, a 3D scene is represented as a light field, i.e., a set of rays, each of which has a corresponding color when reaching the image plane. For efficient novel view rendering, we adopt a two-plane parameterization of the light field, where each ray is characterized by a 4D parameter. We then formulate the light field as a 4D function that maps 4D coordinates to corresponding color values. We train a deep fully connected network to optimize this implicit function and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.07112","kind":"arxiv","version":7},"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/2105.07112/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":"2105.07112","created_at":"2026-07-05T04:38:10.661750+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.07112v7","created_at":"2026-07-05T04:38:10.661750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.07112","created_at":"2026-07-05T04:38:10.661750+00:00"},{"alias_kind":"pith_short_12","alias_value":"CLQPJS736JCC","created_at":"2026-07-05T04:38:10.661750+00:00"},{"alias_kind":"pith_short_16","alias_value":"CLQPJS736JCCHGB5","created_at":"2026-07-05T04:38:10.661750+00:00"},{"alias_kind":"pith_short_8","alias_value":"CLQPJS73","created_at":"2026-07-05T04:38:10.661750+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02846","citing_title":"Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU","json":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU.json","graph_json":"https://pith.science/api/pith-number/CLQPJS736JCCHGB5YPS3OANNQU/graph.json","events_json":"https://pith.science/api/pith-number/CLQPJS736JCCHGB5YPS3OANNQU/events.json","paper":"https://pith.science/paper/CLQPJS73"},"agent_actions":{"view_html":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU","download_json":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU.json","view_paper":"https://pith.science/paper/CLQPJS73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.07112&json=true","fetch_graph":"https://pith.science/api/pith-number/CLQPJS736JCCHGB5YPS3OANNQU/graph.json","fetch_events":"https://pith.science/api/pith-number/CLQPJS736JCCHGB5YPS3OANNQU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU/action/storage_attestation","attest_author":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU/action/author_attestation","sign_citation":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU/action/citation_signature","submit_replication":"https://pith.science/pith/CLQPJS736JCCHGB5YPS3OANNQU/action/replication_record"}},"created_at":"2026-07-05T04:38:10.661750+00:00","updated_at":"2026-07-05T04:38:10.661750+00:00"}