{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7D7SPP2L6SUOBEOS27VYAFMAT3","short_pith_number":"pith:7D7SPP2L","schema_version":"1.0","canonical_sha256":"f8ff27bf4bf4a8e091d2d7eb8015809ec3658edb2fe6e09aba861d3b40ef1a70","source":{"kind":"arxiv","id":"2507.17440","version":1},"attestation_state":"computed","paper":{"title":"Parametric Integration with Neural Integral Operators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Alexander Keller, Christoph Schied","submitted_at":"2025-07-23T12:02:01Z","abstract_excerpt":"Real-time rendering imposes strict limitations on the sampling budget for light transport simulation, often resulting in noisy images. However, denoisers have demonstrated that it is possible to produce noise-free images through filtering. We enhance image quality by removing noise before material shading, rather than filtering already shaded noisy images. This approach allows for material-agnostic denoising (MAD) and leverages machine learning by approximating the light transport integral operator with a neural network, effectively performing parametric integration with neural operators. Our "},"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":"2507.17440","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2025-07-23T12:02:01Z","cross_cats_sorted":[],"title_canon_sha256":"825f9abd625ef422d869b4e3e43ae437e8312a177152f62852f1d4c99bc3a4b7","abstract_canon_sha256":"3a33f9be78ddac9e9343c06cf3dc509d10a09c5252f805deeeb9e7dca3041c89"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:42:07.670662Z","signature_b64":"xyHxf9utqanrQ88b9f4I+Jm4p6nNNNX9+r0oHzFIAf8PRUkuJtR/nWnOZk3ccVVj5M5lcuH1Kq5fz2Lxfq1IBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8ff27bf4bf4a8e091d2d7eb8015809ec3658edb2fe6e09aba861d3b40ef1a70","last_reissued_at":"2026-07-05T11:42:07.670143Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:42:07.670143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parametric Integration with Neural Integral Operators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Alexander Keller, Christoph Schied","submitted_at":"2025-07-23T12:02:01Z","abstract_excerpt":"Real-time rendering imposes strict limitations on the sampling budget for light transport simulation, often resulting in noisy images. However, denoisers have demonstrated that it is possible to produce noise-free images through filtering. We enhance image quality by removing noise before material shading, rather than filtering already shaded noisy images. This approach allows for material-agnostic denoising (MAD) and leverages machine learning by approximating the light transport integral operator with a neural network, effectively performing parametric integration with neural operators. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.17440","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/2507.17440/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":"2507.17440","created_at":"2026-07-05T11:42:07.670207+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.17440v1","created_at":"2026-07-05T11:42:07.670207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.17440","created_at":"2026-07-05T11:42:07.670207+00:00"},{"alias_kind":"pith_short_12","alias_value":"7D7SPP2L6SUO","created_at":"2026-07-05T11:42:07.670207+00:00"},{"alias_kind":"pith_short_16","alias_value":"7D7SPP2L6SUOBEOS","created_at":"2026-07-05T11:42:07.670207+00:00"},{"alias_kind":"pith_short_8","alias_value":"7D7SPP2L","created_at":"2026-07-05T11:42:07.670207+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/7D7SPP2L6SUOBEOS27VYAFMAT3","json":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3.json","graph_json":"https://pith.science/api/pith-number/7D7SPP2L6SUOBEOS27VYAFMAT3/graph.json","events_json":"https://pith.science/api/pith-number/7D7SPP2L6SUOBEOS27VYAFMAT3/events.json","paper":"https://pith.science/paper/7D7SPP2L"},"agent_actions":{"view_html":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3","download_json":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3.json","view_paper":"https://pith.science/paper/7D7SPP2L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.17440&json=true","fetch_graph":"https://pith.science/api/pith-number/7D7SPP2L6SUOBEOS27VYAFMAT3/graph.json","fetch_events":"https://pith.science/api/pith-number/7D7SPP2L6SUOBEOS27VYAFMAT3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3/action/storage_attestation","attest_author":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3/action/author_attestation","sign_citation":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3/action/citation_signature","submit_replication":"https://pith.science/pith/7D7SPP2L6SUOBEOS27VYAFMAT3/action/replication_record"}},"created_at":"2026-07-05T11:42:07.670207+00:00","updated_at":"2026-07-05T11:42:07.670207+00:00"}