{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:UIYVS7HJSVQLOG2UHL5FOHJHBE","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":"8155f0f17c056e3f3147b270149435f452aff87afee4e22849d21cc038fd4961","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-01-13T20:03:06Z","title_canon_sha256":"11bfa2bc678e81e682783e3ff3bbf32ac127b27704d7e71c9ef135518168e6f6"},"schema_version":"1.0","source":{"id":"2301.05747","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.05747","created_at":"2026-07-05T05:32:57Z"},{"alias_kind":"arxiv_version","alias_value":"2301.05747v1","created_at":"2026-07-05T05:32:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.05747","created_at":"2026-07-05T05:32:57Z"},{"alias_kind":"pith_short_12","alias_value":"UIYVS7HJSVQL","created_at":"2026-07-05T05:32:57Z"},{"alias_kind":"pith_short_16","alias_value":"UIYVS7HJSVQLOG2U","created_at":"2026-07-05T05:32:57Z"},{"alias_kind":"pith_short_8","alias_value":"UIYVS7HJ","created_at":"2026-07-05T05:32:57Z"}],"graph_snapshots":[{"event_id":"sha256:174c899d2fde7633aae013807fe120c8332c4801e3c694c2fb5e325764ce5154","target":"graph","created_at":"2026-07-05T05:32:57Z","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/2301.05747/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"NeRF provides unparalleled fidelity of novel view synthesis: rendering a 3D scene from an arbitrary viewpoint. NeRF requires training on a large number of views that fully cover a scene, which limits its applicability. While these issues can be addressed by learning a prior over scenes in various forms, previous approaches have been either applied to overly simple scenes or struggling to render unobserved parts. We introduce Laser-NV: a generative model which achieves high modelling capacity, and which is based on a set-valued latent representation modelled by normalizing flows. Similarly to p","authors_text":"Adam R. Kosiorek, Bj\\\"orn Winckler, Daniel Zoran, Danilo J. Rezende, Heiko Strathmann, Larisa Markeeva, Pol Moreno, Rosalia G. Schneider, Th\\'eophane Weber","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-01-13T20:03:06Z","title":"Laser: Latent Set Representations for 3D Generative Modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.05747","kind":"arxiv","version":1},"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":"sha256:955308523c35d29433cd4ed88eeff5a5c934f36430d068df2be88c4dbd2025fe","target":"record","created_at":"2026-07-05T05:32:57Z","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":"8155f0f17c056e3f3147b270149435f452aff87afee4e22849d21cc038fd4961","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-01-13T20:03:06Z","title_canon_sha256":"11bfa2bc678e81e682783e3ff3bbf32ac127b27704d7e71c9ef135518168e6f6"},"schema_version":"1.0","source":{"id":"2301.05747","kind":"arxiv","version":1}},"canonical_sha256":"a231597ce99560b71b543afa571d2709319c86efbc6b2e06da146b564df1e2b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a231597ce99560b71b543afa571d2709319c86efbc6b2e06da146b564df1e2b1","first_computed_at":"2026-07-05T05:32:57.283277Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:32:57.283277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"13EGzM/ocKfpdY4AIpQPNNdnK1ydBtPafCdwURCN4lD5Ffg631Z3EIWS9GfAapj1BixmdwHkNaAwIcpIZHZNAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:32:57.283752Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.05747","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:955308523c35d29433cd4ed88eeff5a5c934f36430d068df2be88c4dbd2025fe","sha256:174c899d2fde7633aae013807fe120c8332c4801e3c694c2fb5e325764ce5154"],"state_sha256":"917a4d273d0225cd7645bdc50ba12d782e04ee0ef8ac4d253e0d45bf3826174d"}