{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:3HW5QA4TRDGOSNITPOHN7K43HK","short_pith_number":"pith:3HW5QA4T","schema_version":"1.0","canonical_sha256":"d9edd8039388cce935137b8edfab9b3a970b7b49d769f4a4290f95f6e291828e","source":{"kind":"arxiv","id":"1912.06704","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical Deep Stereo Matching on High-resolution Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Deva Ramanan, Gengshan Yang, Joshua Manela, Michael Happold","submitted_at":"2019-12-13T20:57:15Z","abstract_excerpt":"We explore the problem of real-time stereo matching on high-res imagery. Many state-of-the-art (SOTA) methods struggle to process high-res imagery because of memory constraints or speed limitations. To address this issue, we propose an end-to-end framework that searches for correspondences incrementally over a coarse-to-fine hierarchy. Because high-res stereo datasets are relatively rare, we introduce a dataset with high-res stereo pairs for both training and evaluation. Our approach achieved SOTA performance on Middlebury-v3 and KITTI-15 while running significantly faster than its competitors"},"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":"1912.06704","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-12-13T20:57:15Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"2e199a968d992c0f62b140404f49aa25d416cfa05a24dbbeae7050020a9332c8","abstract_canon_sha256":"7d147cc2ebb632dd3aea9c4dee11e70822b59da259f4754a61e34dae64c96897"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:26:13.496381Z","signature_b64":"QHlcWpx7cNgvy8SkOdap49BvhhsPRKFtDoIFAISfgkxaObxkCjyRhN/PZ+irvsRaL1ZeByAt3XV3WUrxjmSCAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9edd8039388cce935137b8edfab9b3a970b7b49d769f4a4290f95f6e291828e","last_reissued_at":"2026-07-05T00:26:13.495951Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:26:13.495951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Deep Stereo Matching on High-resolution Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Deva Ramanan, Gengshan Yang, Joshua Manela, Michael Happold","submitted_at":"2019-12-13T20:57:15Z","abstract_excerpt":"We explore the problem of real-time stereo matching on high-res imagery. Many state-of-the-art (SOTA) methods struggle to process high-res imagery because of memory constraints or speed limitations. To address this issue, we propose an end-to-end framework that searches for correspondences incrementally over a coarse-to-fine hierarchy. Because high-res stereo datasets are relatively rare, we introduce a dataset with high-res stereo pairs for both training and evaluation. Our approach achieved SOTA performance on Middlebury-v3 and KITTI-15 while running significantly faster than its competitors"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.06704","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/1912.06704/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":"1912.06704","created_at":"2026-07-05T00:26:13.496020+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.06704v1","created_at":"2026-07-05T00:26:13.496020+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.06704","created_at":"2026-07-05T00:26:13.496020+00:00"},{"alias_kind":"pith_short_12","alias_value":"3HW5QA4TRDGO","created_at":"2026-07-05T00:26:13.496020+00:00"},{"alias_kind":"pith_short_16","alias_value":"3HW5QA4TRDGOSNIT","created_at":"2026-07-05T00:26:13.496020+00:00"},{"alias_kind":"pith_short_8","alias_value":"3HW5QA4T","created_at":"2026-07-05T00:26:13.496020+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/3HW5QA4TRDGOSNITPOHN7K43HK","json":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK.json","graph_json":"https://pith.science/api/pith-number/3HW5QA4TRDGOSNITPOHN7K43HK/graph.json","events_json":"https://pith.science/api/pith-number/3HW5QA4TRDGOSNITPOHN7K43HK/events.json","paper":"https://pith.science/paper/3HW5QA4T"},"agent_actions":{"view_html":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK","download_json":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK.json","view_paper":"https://pith.science/paper/3HW5QA4T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.06704&json=true","fetch_graph":"https://pith.science/api/pith-number/3HW5QA4TRDGOSNITPOHN7K43HK/graph.json","fetch_events":"https://pith.science/api/pith-number/3HW5QA4TRDGOSNITPOHN7K43HK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK/action/storage_attestation","attest_author":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK/action/author_attestation","sign_citation":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK/action/citation_signature","submit_replication":"https://pith.science/pith/3HW5QA4TRDGOSNITPOHN7K43HK/action/replication_record"}},"created_at":"2026-07-05T00:26:13.496020+00:00","updated_at":"2026-07-05T00:26:13.496020+00:00"}