{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4TJ5DXGLJ3JWQVVIL4YWTR77LP","short_pith_number":"pith:4TJ5DXGL","schema_version":"1.0","canonical_sha256":"e4d3d1dccb4ed36856a85f3169c7ff5bd837437f72165fc8349bc6efa8072a94","source":{"kind":"arxiv","id":"2406.00598","version":1},"attestation_state":"computed","paper":{"title":"Efficient Neural Light Fields (ENeLF) for Mobile Devices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Austin Peng","submitted_at":"2024-06-02T02:55:52Z","abstract_excerpt":"Novel view synthesis (NVS) is a challenge in computer vision and graphics, focusing on generating realistic images of a scene from unobserved camera poses, given a limited set of authentic input images. Neural radiance fields (NeRF) achieved impressive results in rendering quality by utilizing volumetric rendering. However, NeRF and its variants are unsuitable for mobile devices due to the high computational cost of volumetric rendering. Emerging research in neural light fields (NeLF) eliminates the need for volumetric rendering by directly learning a mapping from ray representation to pixel c"},"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":"2406.00598","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-02T02:55:52Z","cross_cats_sorted":[],"title_canon_sha256":"7d51460af9f98c203794136aba09fb758b33199064f51b6844bd86dffefd20b7","abstract_canon_sha256":"b2ddd3ee708e202b9d8a2b103d67260287d766c002c4a42eae6e5d4b865595fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:26:27.825254Z","signature_b64":"gLaJjOc2Gjtb68h1WjR4UzzT+t0A37sRSp92CZUjaQpmHXxPGzNcMFjLiEXrDUscT/YmbCDnMd7VDjKHb432Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4d3d1dccb4ed36856a85f3169c7ff5bd837437f72165fc8349bc6efa8072a94","last_reissued_at":"2026-07-05T08:26:27.824834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:26:27.824834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Neural Light Fields (ENeLF) for Mobile Devices","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Austin Peng","submitted_at":"2024-06-02T02:55:52Z","abstract_excerpt":"Novel view synthesis (NVS) is a challenge in computer vision and graphics, focusing on generating realistic images of a scene from unobserved camera poses, given a limited set of authentic input images. Neural radiance fields (NeRF) achieved impressive results in rendering quality by utilizing volumetric rendering. However, NeRF and its variants are unsuitable for mobile devices due to the high computational cost of volumetric rendering. Emerging research in neural light fields (NeLF) eliminates the need for volumetric rendering by directly learning a mapping from ray representation to pixel c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.00598","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/2406.00598/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":"2406.00598","created_at":"2026-07-05T08:26:27.824889+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.00598v1","created_at":"2026-07-05T08:26:27.824889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.00598","created_at":"2026-07-05T08:26:27.824889+00:00"},{"alias_kind":"pith_short_12","alias_value":"4TJ5DXGLJ3JW","created_at":"2026-07-05T08:26:27.824889+00:00"},{"alias_kind":"pith_short_16","alias_value":"4TJ5DXGLJ3JWQVVI","created_at":"2026-07-05T08:26:27.824889+00:00"},{"alias_kind":"pith_short_8","alias_value":"4TJ5DXGL","created_at":"2026-07-05T08:26:27.824889+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.12208","citing_title":"AI-Driven Innovations in Volumetric Video Streaming: A Review","ref_index":79,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP","json":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP.json","graph_json":"https://pith.science/api/pith-number/4TJ5DXGLJ3JWQVVIL4YWTR77LP/graph.json","events_json":"https://pith.science/api/pith-number/4TJ5DXGLJ3JWQVVIL4YWTR77LP/events.json","paper":"https://pith.science/paper/4TJ5DXGL"},"agent_actions":{"view_html":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP","download_json":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP.json","view_paper":"https://pith.science/paper/4TJ5DXGL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.00598&json=true","fetch_graph":"https://pith.science/api/pith-number/4TJ5DXGLJ3JWQVVIL4YWTR77LP/graph.json","fetch_events":"https://pith.science/api/pith-number/4TJ5DXGLJ3JWQVVIL4YWTR77LP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP/action/storage_attestation","attest_author":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP/action/author_attestation","sign_citation":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP/action/citation_signature","submit_replication":"https://pith.science/pith/4TJ5DXGLJ3JWQVVIL4YWTR77LP/action/replication_record"}},"created_at":"2026-07-05T08:26:27.824889+00:00","updated_at":"2026-07-05T08:26:27.824889+00:00"}