{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LHC6QSZZCQJZYSCBXQTYDVIQKY","short_pith_number":"pith:LHC6QSZZ","schema_version":"1.0","canonical_sha256":"59c5e84b3914139c4841bc2781d5105606edaee8d2f0e7fe51c9dfcaf8e1e822","source":{"kind":"arxiv","id":"2210.12682","version":1},"attestation_state":"computed","paper":{"title":"Photo-realistic Neural Domain Randomization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Rares Ambrus, Sergey Zakharov, Vitor Guizilini, Wadim Kehl","submitted_at":"2022-10-23T09:45:27Z","abstract_excerpt":"Synthetic data is a scalable alternative to manual supervision, but it requires overcoming the sim-to-real domain gap. This discrepancy between virtual and real worlds is addressed by two seemingly opposed approaches: improving the realism of simulation or foregoing realism entirely via domain randomization. In this paper, we show that the recent progress in neural rendering enables a new unified approach we call Photo-realistic Neural Domain Randomization (PNDR). We propose to learn a composition of neural networks that acts as a physics-based ray tracer generating high-quality renderings fro"},"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":"2210.12682","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-23T09:45:27Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"9388fcfadb1b486ce2e09e581064ee5de4d8d5ee85d1609a7d193047d4aa9d23","abstract_canon_sha256":"e2719cdc7559b66492588c49f1a9a5154ba615b07419568e31bde2d49c8ff11d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:31.623302Z","signature_b64":"FtszEtt8Ok/NOrgdBpwYKiYpEr4dQ9XGgg7uY8wlKSfmlJACV9jCCJ78xwp7QLOQ74+z31UAmuacAELISpoCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59c5e84b3914139c4841bc2781d5105606edaee8d2f0e7fe51c9dfcaf8e1e822","last_reissued_at":"2026-07-05T05:09:31.622812Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:31.622812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Photo-realistic Neural Domain Randomization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Rares Ambrus, Sergey Zakharov, Vitor Guizilini, Wadim Kehl","submitted_at":"2022-10-23T09:45:27Z","abstract_excerpt":"Synthetic data is a scalable alternative to manual supervision, but it requires overcoming the sim-to-real domain gap. This discrepancy between virtual and real worlds is addressed by two seemingly opposed approaches: improving the realism of simulation or foregoing realism entirely via domain randomization. In this paper, we show that the recent progress in neural rendering enables a new unified approach we call Photo-realistic Neural Domain Randomization (PNDR). We propose to learn a composition of neural networks that acts as a physics-based ray tracer generating high-quality renderings fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.12682","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/2210.12682/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":"2210.12682","created_at":"2026-07-05T05:09:31.622875+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.12682v1","created_at":"2026-07-05T05:09:31.622875+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.12682","created_at":"2026-07-05T05:09:31.622875+00:00"},{"alias_kind":"pith_short_12","alias_value":"LHC6QSZZCQJZ","created_at":"2026-07-05T05:09:31.622875+00:00"},{"alias_kind":"pith_short_16","alias_value":"LHC6QSZZCQJZYSCB","created_at":"2026-07-05T05:09:31.622875+00:00"},{"alias_kind":"pith_short_8","alias_value":"LHC6QSZZ","created_at":"2026-07-05T05:09:31.622875+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY","json":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY.json","graph_json":"https://pith.science/api/pith-number/LHC6QSZZCQJZYSCBXQTYDVIQKY/graph.json","events_json":"https://pith.science/api/pith-number/LHC6QSZZCQJZYSCBXQTYDVIQKY/events.json","paper":"https://pith.science/paper/LHC6QSZZ"},"agent_actions":{"view_html":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY","download_json":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY.json","view_paper":"https://pith.science/paper/LHC6QSZZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.12682&json=true","fetch_graph":"https://pith.science/api/pith-number/LHC6QSZZCQJZYSCBXQTYDVIQKY/graph.json","fetch_events":"https://pith.science/api/pith-number/LHC6QSZZCQJZYSCBXQTYDVIQKY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY/action/storage_attestation","attest_author":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY/action/author_attestation","sign_citation":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY/action/citation_signature","submit_replication":"https://pith.science/pith/LHC6QSZZCQJZYSCBXQTYDVIQKY/action/replication_record"}},"created_at":"2026-07-05T05:09:31.622875+00:00","updated_at":"2026-07-05T05:09:31.622875+00:00"}