{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NXJ4MLMJNYZPCARJHQAAV6YE2R","short_pith_number":"pith:NXJ4MLMJ","schema_version":"1.0","canonical_sha256":"6dd3c62d896e32f102293c000afb04d47c7e01f0913d77c35bda5e3f322c1c4e","source":{"kind":"arxiv","id":"1803.05530","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Monocular Image Depth Learning and Confidence Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Long Chen, Nigel John, Wen Tang","submitted_at":"2018-03-14T22:59:01Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks. We propose a novel framework for depth estimation from monocular images with corresponding confidence in a self-supervised manner. A fully differential patch-based cost function is proposed by using the Zero-Mean Normalized Cross Correlation (ZNCC) that takes multi-scale patches as a matching strategy. This approach greatly increases the accuracy and robustness of the depth learn"},"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":"1803.05530","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-03-14T22:59:01Z","cross_cats_sorted":[],"title_canon_sha256":"97ec9c226b3c68d531c97d231943cdcea2f5ddbd657453725932363945da8f53","abstract_canon_sha256":"e554dcbda0068c3fcbe7b61e1e3ee960e94073a6f6c8f0b1c75f22f17cb00ead"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:55.202647Z","signature_b64":"aj6q6OfRiFY6PPPmDaANh0yfbYXxwxmvkDbvvT1297cdIOeIKdRk3Z0B9tONUC/P3+izCJO9+mEO0Yl52R3jCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6dd3c62d896e32f102293c000afb04d47c7e01f0913d77c35bda5e3f322c1c4e","last_reissued_at":"2026-05-18T00:20:55.202080Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:55.202080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Monocular Image Depth Learning and Confidence Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Long Chen, Nigel John, Wen Tang","submitted_at":"2018-03-14T22:59:01Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks. We propose a novel framework for depth estimation from monocular images with corresponding confidence in a self-supervised manner. A fully differential patch-based cost function is proposed by using the Zero-Mean Normalized Cross Correlation (ZNCC) that takes multi-scale patches as a matching strategy. This approach greatly increases the accuracy and robustness of the depth learn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.05530","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":"1803.05530","created_at":"2026-05-18T00:20:55.202177+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.05530v1","created_at":"2026-05-18T00:20:55.202177+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.05530","created_at":"2026-05-18T00:20:55.202177+00:00"},{"alias_kind":"pith_short_12","alias_value":"NXJ4MLMJNYZP","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NXJ4MLMJNYZPCARJ","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NXJ4MLMJ","created_at":"2026-05-18T12:32:40.477152+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/NXJ4MLMJNYZPCARJHQAAV6YE2R","json":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R.json","graph_json":"https://pith.science/api/pith-number/NXJ4MLMJNYZPCARJHQAAV6YE2R/graph.json","events_json":"https://pith.science/api/pith-number/NXJ4MLMJNYZPCARJHQAAV6YE2R/events.json","paper":"https://pith.science/paper/NXJ4MLMJ"},"agent_actions":{"view_html":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R","download_json":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R.json","view_paper":"https://pith.science/paper/NXJ4MLMJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.05530&json=true","fetch_graph":"https://pith.science/api/pith-number/NXJ4MLMJNYZPCARJHQAAV6YE2R/graph.json","fetch_events":"https://pith.science/api/pith-number/NXJ4MLMJNYZPCARJHQAAV6YE2R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R/action/storage_attestation","attest_author":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R/action/author_attestation","sign_citation":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R/action/citation_signature","submit_replication":"https://pith.science/pith/NXJ4MLMJNYZPCARJHQAAV6YE2R/action/replication_record"}},"created_at":"2026-05-18T00:20:55.202177+00:00","updated_at":"2026-05-18T00:20:55.202177+00:00"}