{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MBVDGS4NZOF3DT6EJN4PUOJO6N","short_pith_number":"pith:MBVDGS4N","schema_version":"1.0","canonical_sha256":"606a334b8dcb8bb1cfc44b78fa392ef350bf87e939d5204d6560c1cbb7701495","source":{"kind":"arxiv","id":"2406.05475","version":2},"attestation_state":"computed","paper":{"title":"HDRT: A Large-Scale Dataset for Infrared-Guided HDR Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","eess.IV"],"primary_cat":"cs.CV","authors_text":"Francesco Banterle, Haitao Zhao, Jingchao Peng, Kurt Debattista, Thomas Bashford-Rogers","submitted_at":"2024-06-08T13:43:44Z","abstract_excerpt":"Capturing images with enough details to solve imaging tasks is a long-standing challenge in imaging, particularly due to the limitations of standard dynamic range (SDR) images which often lose details in underexposed or overexposed regions. Traditional high dynamic range (HDR) methods, like multi-exposure fusion or inverse tone mapping, struggle with ghosting and incomplete data reconstruction. Infrared (IR) imaging offers a unique advantage by being less affected by lighting conditions, providing consistent detail capture regardless of visible light intensity. In this paper, we introduce the "},"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":"2406.05475","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-08T13:43:44Z","cross_cats_sorted":["cs.GR","eess.IV"],"title_canon_sha256":"96abc62f9c73ea9c2db3a75967ba1c0c7cbcf8930ccf331deb4b6563049c2e99","abstract_canon_sha256":"bd7bd5d38ed4c9ea7f9cb2d316f8e34fb582870fbcd7b99b7f1dfdd9afab17fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:27.117348Z","signature_b64":"6WILYFtvXLX45Yvg9WiNMC8rxJ/UQG8XcmDnQ0m7P6QtH4CpvO24TEYxLUVeXdzLgcuYNtyAFEijy9K1+0UDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"606a334b8dcb8bb1cfc44b78fa392ef350bf87e939d5204d6560c1cbb7701495","last_reissued_at":"2026-07-05T10:37:27.116603Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:27.116603Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HDRT: A Large-Scale Dataset for Infrared-Guided HDR Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","eess.IV"],"primary_cat":"cs.CV","authors_text":"Francesco Banterle, Haitao Zhao, Jingchao Peng, Kurt Debattista, Thomas Bashford-Rogers","submitted_at":"2024-06-08T13:43:44Z","abstract_excerpt":"Capturing images with enough details to solve imaging tasks is a long-standing challenge in imaging, particularly due to the limitations of standard dynamic range (SDR) images which often lose details in underexposed or overexposed regions. Traditional high dynamic range (HDR) methods, like multi-exposure fusion or inverse tone mapping, struggle with ghosting and incomplete data reconstruction. Infrared (IR) imaging offers a unique advantage by being less affected by lighting conditions, providing consistent detail capture regardless of visible light intensity. In this paper, we introduce the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05475","kind":"arxiv","version":2},"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/2406.05475/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":"2406.05475","created_at":"2026-07-05T10:37:27.116714+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05475v2","created_at":"2026-07-05T10:37:27.116714+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05475","created_at":"2026-07-05T10:37:27.116714+00:00"},{"alias_kind":"pith_short_12","alias_value":"MBVDGS4NZOF3","created_at":"2026-07-05T10:37:27.116714+00:00"},{"alias_kind":"pith_short_16","alias_value":"MBVDGS4NZOF3DT6E","created_at":"2026-07-05T10:37:27.116714+00:00"},{"alias_kind":"pith_short_8","alias_value":"MBVDGS4N","created_at":"2026-07-05T10:37:27.116714+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.16327","citing_title":"CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N","json":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N.json","graph_json":"https://pith.science/api/pith-number/MBVDGS4NZOF3DT6EJN4PUOJO6N/graph.json","events_json":"https://pith.science/api/pith-number/MBVDGS4NZOF3DT6EJN4PUOJO6N/events.json","paper":"https://pith.science/paper/MBVDGS4N"},"agent_actions":{"view_html":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N","download_json":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N.json","view_paper":"https://pith.science/paper/MBVDGS4N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05475&json=true","fetch_graph":"https://pith.science/api/pith-number/MBVDGS4NZOF3DT6EJN4PUOJO6N/graph.json","fetch_events":"https://pith.science/api/pith-number/MBVDGS4NZOF3DT6EJN4PUOJO6N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N/action/storage_attestation","attest_author":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N/action/author_attestation","sign_citation":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N/action/citation_signature","submit_replication":"https://pith.science/pith/MBVDGS4NZOF3DT6EJN4PUOJO6N/action/replication_record"}},"created_at":"2026-07-05T10:37:27.116714+00:00","updated_at":"2026-07-05T10:37:27.116714+00:00"}