{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:N3ABJAD4V5RXDMVXC3MK3JKMD7","short_pith_number":"pith:N3ABJAD4","canonical_record":{"source":{"id":"2306.11920","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-20T22:06:39Z","cross_cats_sorted":[],"title_canon_sha256":"58bc604aaa472d2d2a3b1f8e83001f856a41aaf267fa39c4c5d688ebcc9ca90b","abstract_canon_sha256":"a8e5ca2fe24762248b500ebc44ec87a374792d50083e84293b10a63ec206dbf7"},"schema_version":"1.0"},"canonical_sha256":"6ec014807caf6371b2b716d8ada54c1fc5894b997060bfaea47b9e7872f83701","source":{"kind":"arxiv","id":"2306.11920","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11920","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11920v3","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11920","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"pith_short_12","alias_value":"N3ABJAD4V5RX","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"pith_short_16","alias_value":"N3ABJAD4V5RXDMVX","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"pith_short_8","alias_value":"N3ABJAD4","created_at":"2026-07-05T07:27:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:N3ABJAD4V5RXDMVXC3MK3JKMD7","target":"record","payload":{"canonical_record":{"source":{"id":"2306.11920","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-20T22:06:39Z","cross_cats_sorted":[],"title_canon_sha256":"58bc604aaa472d2d2a3b1f8e83001f856a41aaf267fa39c4c5d688ebcc9ca90b","abstract_canon_sha256":"a8e5ca2fe24762248b500ebc44ec87a374792d50083e84293b10a63ec206dbf7"},"schema_version":"1.0"},"canonical_sha256":"6ec014807caf6371b2b716d8ada54c1fc5894b997060bfaea47b9e7872f83701","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:47.430420Z","signature_b64":"BV1Aaz2T10U9N63K18kw8HMYI5/ewvYMfxlX0k8WOv8zAoRWwgMQbYp6WIlFWMkmtspNWfZ8fJVsb4x2b02HBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ec014807caf6371b2b716d8ada54c1fc5894b997060bfaea47b9e7872f83701","last_reissued_at":"2026-07-05T07:27:47.429880Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:47.429880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.11920","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:27:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zLhAKBWTpw2DruBe9F3TJWN7pnwZVBwjNkZnKs2hHxudq/rp45dB4Qb/gRBdwX9gk0J4dL45KSCra6CwzKlZDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:23:24.360833Z"},"content_sha256":"a7c55fffb038e6f172a9c161ba852f256bb55867c978a087183080f3f7a7842e","schema_version":"1.0","event_id":"sha256:a7c55fffb038e6f172a9c161ba852f256bb55867c978a087183080f3f7a7842e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:N3ABJAD4V5RXDMVXC3MK3JKMD7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Javier Vazquez-Corral, Marcos V. Conde, Michael S. Brown, Radu Timofte","submitted_at":"2023-06-20T22:06:39Z","abstract_excerpt":"3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11920","kind":"arxiv","version":3},"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/2306.11920/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:27:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hiv8G8/crGFOhRDrBXftw8Ao0Vp73e3T+1JDyRWGFJEY6cz4EF4oTq5g/KkknSytYjGlGxuUFemeIBtvkQqvBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T19:23:24.361341Z"},"content_sha256":"92cda5cd019f418e1e2130c0f8a451b0ca2b40edabe8082cd92c1eef4435ad09","schema_version":"1.0","event_id":"sha256:92cda5cd019f418e1e2130c0f8a451b0ca2b40edabe8082cd92c1eef4435ad09"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N3ABJAD4V5RXDMVXC3MK3JKMD7/bundle.json","state_url":"https://pith.science/pith/N3ABJAD4V5RXDMVXC3MK3JKMD7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N3ABJAD4V5RXDMVXC3MK3JKMD7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T19:23:24Z","links":{"resolver":"https://pith.science/pith/N3ABJAD4V5RXDMVXC3MK3JKMD7","bundle":"https://pith.science/pith/N3ABJAD4V5RXDMVXC3MK3JKMD7/bundle.json","state":"https://pith.science/pith/N3ABJAD4V5RXDMVXC3MK3JKMD7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N3ABJAD4V5RXDMVXC3MK3JKMD7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:N3ABJAD4V5RXDMVXC3MK3JKMD7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a8e5ca2fe24762248b500ebc44ec87a374792d50083e84293b10a63ec206dbf7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-20T22:06:39Z","title_canon_sha256":"58bc604aaa472d2d2a3b1f8e83001f856a41aaf267fa39c4c5d688ebcc9ca90b"},"schema_version":"1.0","source":{"id":"2306.11920","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11920","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11920v3","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11920","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"pith_short_12","alias_value":"N3ABJAD4V5RX","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"pith_short_16","alias_value":"N3ABJAD4V5RXDMVX","created_at":"2026-07-05T07:27:47Z"},{"alias_kind":"pith_short_8","alias_value":"N3ABJAD4","created_at":"2026-07-05T07:27:47Z"}],"graph_snapshots":[{"event_id":"sha256:92cda5cd019f418e1e2130c0f8a451b0ca2b40edabe8082cd92c1eef4435ad09","target":"graph","created_at":"2026-07-05T07:27:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.11920/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Im","authors_text":"Javier Vazquez-Corral, Marcos V. Conde, Michael S. Brown, Radu Timofte","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-20T22:06:39Z","title":"NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11920","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a7c55fffb038e6f172a9c161ba852f256bb55867c978a087183080f3f7a7842e","target":"record","created_at":"2026-07-05T07:27:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a8e5ca2fe24762248b500ebc44ec87a374792d50083e84293b10a63ec206dbf7","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-20T22:06:39Z","title_canon_sha256":"58bc604aaa472d2d2a3b1f8e83001f856a41aaf267fa39c4c5d688ebcc9ca90b"},"schema_version":"1.0","source":{"id":"2306.11920","kind":"arxiv","version":3}},"canonical_sha256":"6ec014807caf6371b2b716d8ada54c1fc5894b997060bfaea47b9e7872f83701","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ec014807caf6371b2b716d8ada54c1fc5894b997060bfaea47b9e7872f83701","first_computed_at":"2026-07-05T07:27:47.429880Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:27:47.429880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BV1Aaz2T10U9N63K18kw8HMYI5/ewvYMfxlX0k8WOv8zAoRWwgMQbYp6WIlFWMkmtspNWfZ8fJVsb4x2b02HBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:27:47.430420Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.11920","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a7c55fffb038e6f172a9c161ba852f256bb55867c978a087183080f3f7a7842e","sha256:92cda5cd019f418e1e2130c0f8a451b0ca2b40edabe8082cd92c1eef4435ad09"],"state_sha256":"c06b4c07d559600346dc568494ce16966e216e2a058c4decb11b7de185ecbab5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"smKv7ilxT3KejCL0tnTFpzJ+WaJ4PyTXunCZDCmBSBr2bo6ldsgBVI0Yxt+kIzAA+8hOog5t68RhyCEglopcDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T19:23:24.365776Z","bundle_sha256":"cac5ccf1dfc3a0d222ef1bd9fd87faa622116f80e5ced8fecce46fd5241e9e77"}}