{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JY2GSFYEV4GEUY425OBMPBMNS7","short_pith_number":"pith:JY2GSFYE","schema_version":"1.0","canonical_sha256":"4e34691704af0c4a639aeb82c7858d97f15ceff5c9f341ea89f00ca9df8e0f7a","source":{"kind":"arxiv","id":"2403.11865","version":2},"attestation_state":"computed","paper":{"title":"Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Bernhard Egger, Martin Hundhausen, Maximilian Weiherer, Mert \\\"Ozer","submitted_at":"2024-03-18T15:18:55Z","abstract_excerpt":"Neural Radiance Fields (NeRFs) quickly evolved as the new de-facto standard for the task of novel view synthesis when trained on a set of RGB images. In this paper, we conduct a comprehensive evaluation of neural scene representations, such as NeRFs, in the context of multi-modal learning. Specifically, we present four different strategies of how to incorporate a second modality, other than RGB, into NeRFs: (1) training from scratch independently on both modalities; (2) pre-training on RGB and fine-tuning on the second modality; (3) adding a second branch; and (4) adding a separate component t"},"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":"2403.11865","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-18T15:18:55Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"119297c5456c5a6050b28863748e2d96ab7f19a87f6062a8a1e26c241bf47df9","abstract_canon_sha256":"151c5f7e507f952481aa250bca11a210298a2ca362c961255cd51c545de742b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:22.911435Z","signature_b64":"vBeyoSnSRUy6iZAWXBXnFlbXy/FH9ISEFhQHDSflYCiFcjpsYdCkwevtN9i0Q+JNDwGotmmoNjWGGiioPVeDDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e34691704af0c4a639aeb82c7858d97f15ceff5c9f341ea89f00ca9df8e0f7a","last_reissued_at":"2026-07-05T08:58:22.910934Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:22.910934Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Multi-modal Neural Scene Representations With Applications on Thermal Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Bernhard Egger, Martin Hundhausen, Maximilian Weiherer, Mert \\\"Ozer","submitted_at":"2024-03-18T15:18:55Z","abstract_excerpt":"Neural Radiance Fields (NeRFs) quickly evolved as the new de-facto standard for the task of novel view synthesis when trained on a set of RGB images. In this paper, we conduct a comprehensive evaluation of neural scene representations, such as NeRFs, in the context of multi-modal learning. Specifically, we present four different strategies of how to incorporate a second modality, other than RGB, into NeRFs: (1) training from scratch independently on both modalities; (2) pre-training on RGB and fine-tuning on the second modality; (3) adding a second branch; and (4) adding a separate component t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11865","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/2403.11865/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":"2403.11865","created_at":"2026-07-05T08:58:22.911002+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.11865v2","created_at":"2026-07-05T08:58:22.911002+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11865","created_at":"2026-07-05T08:58:22.911002+00:00"},{"alias_kind":"pith_short_12","alias_value":"JY2GSFYEV4GE","created_at":"2026-07-05T08:58:22.911002+00:00"},{"alias_kind":"pith_short_16","alias_value":"JY2GSFYEV4GEUY42","created_at":"2026-07-05T08:58:22.911002+00:00"},{"alias_kind":"pith_short_8","alias_value":"JY2GSFYE","created_at":"2026-07-05T08:58:22.911002+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26328","citing_title":"RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7","json":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7.json","graph_json":"https://pith.science/api/pith-number/JY2GSFYEV4GEUY425OBMPBMNS7/graph.json","events_json":"https://pith.science/api/pith-number/JY2GSFYEV4GEUY425OBMPBMNS7/events.json","paper":"https://pith.science/paper/JY2GSFYE"},"agent_actions":{"view_html":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7","download_json":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7.json","view_paper":"https://pith.science/paper/JY2GSFYE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.11865&json=true","fetch_graph":"https://pith.science/api/pith-number/JY2GSFYEV4GEUY425OBMPBMNS7/graph.json","fetch_events":"https://pith.science/api/pith-number/JY2GSFYEV4GEUY425OBMPBMNS7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7/action/storage_attestation","attest_author":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7/action/author_attestation","sign_citation":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7/action/citation_signature","submit_replication":"https://pith.science/pith/JY2GSFYEV4GEUY425OBMPBMNS7/action/replication_record"}},"created_at":"2026-07-05T08:58:22.911002+00:00","updated_at":"2026-07-05T08:58:22.911002+00:00"}