{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZV4ULLO27VC4FTRKU5MGGBSQPX","short_pith_number":"pith:ZV4ULLO2","schema_version":"1.0","canonical_sha256":"cd7945addafd45c2ce2aa7586306507de332813ed92c29d880e6d653fc599529","source":{"kind":"arxiv","id":"1906.01618","version":2},"attestation_state":"computed","paper":{"title":"Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gordon Wetzstein, Michael Zollh\\\"ofer, Vincent Sitzmann","submitted_at":"2019-06-04T17:53:11Z","abstract_excerpt":"Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous f"},"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":"1906.01618","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-04T17:53:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d8c89447ea6976b2bc9a6d0234684e07e168fed7bdc1afa97d5a1af206b7d691","abstract_canon_sha256":"6e23cb371c97a0e1fb32282d94d64977ca9fb19745bfa2ad788a63a8c0ea18e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:37:07.220892Z","signature_b64":"jj5WYbar3pvaVCAbRZB/NHnbvsgjdnxpiEjZDrkXrz5ybI09Ybc4wHHM4RGHsRisLY15lkDA5vRsh0w0TnFvCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd7945addafd45c2ce2aa7586306507de332813ed92c29d880e6d653fc599529","last_reissued_at":"2026-07-05T00:37:07.220353Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:37:07.220353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gordon Wetzstein, Michael Zollh\\\"ofer, Vincent Sitzmann","submitted_at":"2019-06-04T17:53:11Z","abstract_excerpt":"Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01618","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/1906.01618/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":"1906.01618","created_at":"2026-07-05T00:37:07.220415+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.01618v2","created_at":"2026-07-05T00:37:07.220415+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01618","created_at":"2026-07-05T00:37:07.220415+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZV4ULLO27VC4","created_at":"2026-07-05T00:37:07.220415+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZV4ULLO27VC4FTRK","created_at":"2026-07-05T00:37:07.220415+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZV4ULLO2","created_at":"2026-07-05T00:37:07.220415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.01288","citing_title":"WishGI: Lightweight Static Global Illumination Baking via Spherical Harmonics Fitting","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX","json":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX.json","graph_json":"https://pith.science/api/pith-number/ZV4ULLO27VC4FTRKU5MGGBSQPX/graph.json","events_json":"https://pith.science/api/pith-number/ZV4ULLO27VC4FTRKU5MGGBSQPX/events.json","paper":"https://pith.science/paper/ZV4ULLO2"},"agent_actions":{"view_html":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX","download_json":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX.json","view_paper":"https://pith.science/paper/ZV4ULLO2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.01618&json=true","fetch_graph":"https://pith.science/api/pith-number/ZV4ULLO27VC4FTRKU5MGGBSQPX/graph.json","fetch_events":"https://pith.science/api/pith-number/ZV4ULLO27VC4FTRKU5MGGBSQPX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX/action/storage_attestation","attest_author":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX/action/author_attestation","sign_citation":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX/action/citation_signature","submit_replication":"https://pith.science/pith/ZV4ULLO27VC4FTRKU5MGGBSQPX/action/replication_record"}},"created_at":"2026-07-05T00:37:07.220415+00:00","updated_at":"2026-07-05T00:37:07.220415+00:00"}