{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:P5DGODOSHPGUWDRFFKZKGV63OT","short_pith_number":"pith:P5DGODOS","schema_version":"1.0","canonical_sha256":"7f46670dd23bcd4b0e252ab2a357db74fe6a163df8b92746a12f9912437750cc","source":{"kind":"arxiv","id":"1910.01765","version":3},"attestation_state":"computed","paper":{"title":"Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Jie Li, Rares Ambrus, Sudeep Pillai, Vitor Guizilini","submitted_at":"2019-10-04T00:32:20Z","abstract_excerpt":"Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression problem, requiring annotated labels as supervision at training time, in the past few years a substantial amount of work has been done in self-supervised depth training based on strong geometric cues, both from stereo cameras and more recently from monocular video sequences. In this paper we investigate how these two approaches (supervised & self-supervised) can be effectively combined, so that a depth model can 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":"1910.01765","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-10-04T00:32:20Z","cross_cats_sorted":[],"title_canon_sha256":"12b407e12fe90c498dbfa807b53dedb9f9b039b1b03c0b4564d2ac366593c6c6","abstract_canon_sha256":"abcc9fda56e175280b549d608870fd8ba9b40aead971895fe61c1252934bef41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:20:20.737229Z","signature_b64":"PeBBnVXbkNrzVgNnzKXS5PAVCK7asTPmSeiaqC7bQon1tAf6n8O5seMBFELFfGRWj68jBuWLnIRPGLRJyiWXAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f46670dd23bcd4b0e252ab2a357db74fe6a163df8b92746a12f9912437750cc","last_reissued_at":"2026-07-05T00:20:20.736784Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:20:20.736784Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrien Gaidon, Jie Li, Rares Ambrus, Sudeep Pillai, Vitor Guizilini","submitted_at":"2019-10-04T00:32:20Z","abstract_excerpt":"Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression problem, requiring annotated labels as supervision at training time, in the past few years a substantial amount of work has been done in self-supervised depth training based on strong geometric cues, both from stereo cameras and more recently from monocular video sequences. In this paper we investigate how these two approaches (supervised & self-supervised) can be effectively combined, so that a depth model can learn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.01765","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1910.01765/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":"1910.01765","created_at":"2026-07-05T00:20:20.736843+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.01765v3","created_at":"2026-07-05T00:20:20.736843+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.01765","created_at":"2026-07-05T00:20:20.736843+00:00"},{"alias_kind":"pith_short_12","alias_value":"P5DGODOSHPGU","created_at":"2026-07-05T00:20:20.736843+00:00"},{"alias_kind":"pith_short_16","alias_value":"P5DGODOSHPGUWDRF","created_at":"2026-07-05T00:20:20.736843+00:00"},{"alias_kind":"pith_short_8","alias_value":"P5DGODOS","created_at":"2026-07-05T00:20:20.736843+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/P5DGODOSHPGUWDRFFKZKGV63OT","json":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT.json","graph_json":"https://pith.science/api/pith-number/P5DGODOSHPGUWDRFFKZKGV63OT/graph.json","events_json":"https://pith.science/api/pith-number/P5DGODOSHPGUWDRFFKZKGV63OT/events.json","paper":"https://pith.science/paper/P5DGODOS"},"agent_actions":{"view_html":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT","download_json":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT.json","view_paper":"https://pith.science/paper/P5DGODOS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.01765&json=true","fetch_graph":"https://pith.science/api/pith-number/P5DGODOSHPGUWDRFFKZKGV63OT/graph.json","fetch_events":"https://pith.science/api/pith-number/P5DGODOSHPGUWDRFFKZKGV63OT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT/action/storage_attestation","attest_author":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT/action/author_attestation","sign_citation":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT/action/citation_signature","submit_replication":"https://pith.science/pith/P5DGODOSHPGUWDRFFKZKGV63OT/action/replication_record"}},"created_at":"2026-07-05T00:20:20.736843+00:00","updated_at":"2026-07-05T00:20:20.736843+00:00"}