{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GW4HS3W3SHA2AUYFFY5NCL27TL","short_pith_number":"pith:GW4HS3W3","schema_version":"1.0","canonical_sha256":"35b8796edb91c1a053052e3ad12f5f9aebc69e70a74cae7f19c2b517af639217","source":{"kind":"arxiv","id":"1806.02446","version":1},"attestation_state":"computed","paper":{"title":"Deep Ordinal Regression Network for Monocular Depth Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaohui Wang, Dacheng Tao, Huan Fu, Kayhan Batmanghelich, Mingming Gong","submitted_at":"2018-06-06T22:36:23Z","abstract_excerpt":"Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable low-res"},"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":"1806.02446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-06T22:36:23Z","cross_cats_sorted":[],"title_canon_sha256":"5d087179f02cf7913a218e3e625e2e0c4370e5263d3dfa11bb47faa8627458f9","abstract_canon_sha256":"0396fa17ebf3636b5e854f928e14fd2be350d2a0d6abac29809696d8ed7d7a8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:56.816137Z","signature_b64":"XPTOZevE4WyABcsecnpmeTqJlsv+2Mukhga7If7J2/6BdrchaVuCzKctS/lkZiMASav7uNH2S81R9jrWaRjzAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35b8796edb91c1a053052e3ad12f5f9aebc69e70a74cae7f19c2b517af639217","last_reissued_at":"2026-05-18T00:13:56.815399Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:56.815399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Ordinal Regression Network for Monocular Depth Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chaohui Wang, Dacheng Tao, Huan Fu, Kayhan Batmanghelich, Mingming Gong","submitted_at":"2018-06-06T22:36:23Z","abstract_excerpt":"Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable low-res"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.02446","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":"1806.02446","created_at":"2026-05-18T00:13:56.815492+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.02446v1","created_at":"2026-05-18T00:13:56.815492+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.02446","created_at":"2026-05-18T00:13:56.815492+00:00"},{"alias_kind":"pith_short_12","alias_value":"GW4HS3W3SHA2","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GW4HS3W3SHA2AUYF","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GW4HS3W3","created_at":"2026-05-18T12:32:25.280505+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/GW4HS3W3SHA2AUYFFY5NCL27TL","json":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL.json","graph_json":"https://pith.science/api/pith-number/GW4HS3W3SHA2AUYFFY5NCL27TL/graph.json","events_json":"https://pith.science/api/pith-number/GW4HS3W3SHA2AUYFFY5NCL27TL/events.json","paper":"https://pith.science/paper/GW4HS3W3"},"agent_actions":{"view_html":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL","download_json":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL.json","view_paper":"https://pith.science/paper/GW4HS3W3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.02446&json=true","fetch_graph":"https://pith.science/api/pith-number/GW4HS3W3SHA2AUYFFY5NCL27TL/graph.json","fetch_events":"https://pith.science/api/pith-number/GW4HS3W3SHA2AUYFFY5NCL27TL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL/action/storage_attestation","attest_author":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL/action/author_attestation","sign_citation":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL/action/citation_signature","submit_replication":"https://pith.science/pith/GW4HS3W3SHA2AUYFFY5NCL27TL/action/replication_record"}},"created_at":"2026-05-18T00:13:56.815492+00:00","updated_at":"2026-05-18T00:13:56.815492+00:00"}