{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:FOMA2V3GMBW2LKUKGSIEY5ZCRT","short_pith_number":"pith:FOMA2V3G","schema_version":"1.0","canonical_sha256":"2b980d5766606da5aa8a34904c77228cd42036d774a4b54d1a3535578ccc1136","source":{"kind":"arxiv","id":"1704.00090","version":3},"attestation_state":"computed","paper":{"title":"Learning to Predict Indoor Illumination from a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","stat.ML"],"primary_cat":"cs.CV","authors_text":"Christian Gagn\\'e, Emiliano Gambaretto, Ersin Yumer, Jean-Fran\\c{c}ois Lalonde, Kalyan Sunkavalli, Marc-Andr\\'e Gardner, Xiaohui Shen","submitted_at":"2017-04-01T00:50:12Z","abstract_excerpt":"We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate th"},"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":"1704.00090","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-01T00:50:12Z","cross_cats_sorted":["cs.GR","stat.ML"],"title_canon_sha256":"92c54e61981229480c6742bd8c6baf2ac697befe8643e95e71b5380ebe4d1ecd","abstract_canon_sha256":"180d9e4f71a848a626a7c02031a5fe5f899cece0c90f5f410df63a75f80c8d3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:05.090283Z","signature_b64":"QMXmYfab8e5+wIyYBY3EL5Y/MbBjQ4ihSGyoyYMueB15zPMOKuGvKbLWMz42vP82qaID/23YRZTnB1tQ15fQCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b980d5766606da5aa8a34904c77228cd42036d774a4b54d1a3535578ccc1136","last_reissued_at":"2026-05-18T00:30:05.089744Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:05.089744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to Predict Indoor Illumination from a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","stat.ML"],"primary_cat":"cs.CV","authors_text":"Christian Gagn\\'e, Emiliano Gambaretto, Ersin Yumer, Jean-Fran\\c{c}ois Lalonde, Kalyan Sunkavalli, Marc-Andr\\'e Gardner, Xiaohui Shen","submitted_at":"2017-04-01T00:50:12Z","abstract_excerpt":"We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.00090","kind":"arxiv","version":3},"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":"1704.00090","created_at":"2026-05-18T00:30:05.089854+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.00090v3","created_at":"2026-05-18T00:30:05.089854+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00090","created_at":"2026-05-18T00:30:05.089854+00:00"},{"alias_kind":"pith_short_12","alias_value":"FOMA2V3GMBW2","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"FOMA2V3GMBW2LKUK","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"FOMA2V3G","created_at":"2026-05-18T12:31:15.632608+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/FOMA2V3GMBW2LKUKGSIEY5ZCRT","json":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT.json","graph_json":"https://pith.science/api/pith-number/FOMA2V3GMBW2LKUKGSIEY5ZCRT/graph.json","events_json":"https://pith.science/api/pith-number/FOMA2V3GMBW2LKUKGSIEY5ZCRT/events.json","paper":"https://pith.science/paper/FOMA2V3G"},"agent_actions":{"view_html":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT","download_json":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT.json","view_paper":"https://pith.science/paper/FOMA2V3G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.00090&json=true","fetch_graph":"https://pith.science/api/pith-number/FOMA2V3GMBW2LKUKGSIEY5ZCRT/graph.json","fetch_events":"https://pith.science/api/pith-number/FOMA2V3GMBW2LKUKGSIEY5ZCRT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT/action/storage_attestation","attest_author":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT/action/author_attestation","sign_citation":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT/action/citation_signature","submit_replication":"https://pith.science/pith/FOMA2V3GMBW2LKUKGSIEY5ZCRT/action/replication_record"}},"created_at":"2026-05-18T00:30:05.089854+00:00","updated_at":"2026-05-18T00:30:05.089854+00:00"}