{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WHOK2WY5YVEEEAEEQDJWWQGVXG","short_pith_number":"pith:WHOK2WY5","schema_version":"1.0","canonical_sha256":"b1dcad5b1dc54842008480d36b40d5b985dde2358958cbe690a874a0c4f28ba1","source":{"kind":"arxiv","id":"1810.09718","version":1},"attestation_state":"computed","paper":{"title":"Single-Image SVBRDF Capture with a Rendering-Aware Deep Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Adrien Bousseau, Fredo Durand, George Drettakis, Miika Aittala, Valentin Deschaintre","submitted_at":"2018-10-23T08:30:34Z","abstract_excerpt":"Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single"},"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":"1810.09718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2018-10-23T08:30:34Z","cross_cats_sorted":[],"title_canon_sha256":"d556c9100a722858ce9ffc4b4da9ebf54fbf4753a932cef79ded3f393450e686","abstract_canon_sha256":"6680c2154049db035347bb7ec67bdd43f3bfbc40c01e750a8084a831f13d1a4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:29.665647Z","signature_b64":"b6+Zpp4ZUeH7B7Yd6pEDDVKZTlapTJd4KHbuLXCqmSoYDDjnCudn34V7pCn79jlQ7Dj3vg1KvfSKriLSddAlAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1dcad5b1dc54842008480d36b40d5b985dde2358958cbe690a874a0c4f28ba1","last_reissued_at":"2026-05-18T00:02:29.664972Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:29.664972Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Single-Image SVBRDF Capture with a Rendering-Aware Deep Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.GR","authors_text":"Adrien Bousseau, Fredo Durand, George Drettakis, Miika Aittala, Valentin Deschaintre","submitted_at":"2018-10-23T08:30:34Z","abstract_excerpt":"Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.09718","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":""},"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":"1810.09718","created_at":"2026-05-18T00:02:29.665069+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.09718v1","created_at":"2026-05-18T00:02:29.665069+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.09718","created_at":"2026-05-18T00:02:29.665069+00:00"},{"alias_kind":"pith_short_12","alias_value":"WHOK2WY5YVEE","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"WHOK2WY5YVEEEAEE","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"WHOK2WY5","created_at":"2026-05-18T12:32:59.047623+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/WHOK2WY5YVEEEAEEQDJWWQGVXG","json":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG.json","graph_json":"https://pith.science/api/pith-number/WHOK2WY5YVEEEAEEQDJWWQGVXG/graph.json","events_json":"https://pith.science/api/pith-number/WHOK2WY5YVEEEAEEQDJWWQGVXG/events.json","paper":"https://pith.science/paper/WHOK2WY5"},"agent_actions":{"view_html":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG","download_json":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG.json","view_paper":"https://pith.science/paper/WHOK2WY5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.09718&json=true","fetch_graph":"https://pith.science/api/pith-number/WHOK2WY5YVEEEAEEQDJWWQGVXG/graph.json","fetch_events":"https://pith.science/api/pith-number/WHOK2WY5YVEEEAEEQDJWWQGVXG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG/action/storage_attestation","attest_author":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG/action/author_attestation","sign_citation":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG/action/citation_signature","submit_replication":"https://pith.science/pith/WHOK2WY5YVEEEAEEQDJWWQGVXG/action/replication_record"}},"created_at":"2026-05-18T00:02:29.665069+00:00","updated_at":"2026-05-18T00:02:29.665069+00:00"}