{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:OSRKC3EXESQMONIUEPP5QMQBEU","short_pith_number":"pith:OSRKC3EX","schema_version":"1.0","canonical_sha256":"74a2a16c9724a0c7351423dfd8320125022897cc9237705bc7bae73b58768dd3","source":{"kind":"arxiv","id":"2002.00569","version":3},"attestation_state":"computed","paper":{"title":"DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changming Sun, Chunhua Shen, Dou Renyin, Songcen Xu, Wei Yin, Xinlong Wang, Yifan Liu, Zhi Tian","submitted_at":"2020-02-03T05:38:33Z","abstract_excerpt":"We present a method for depth estimation with monocular images, which can predict high-quality depth on diverse scenes up to an affine transformation, thus preserving accurate shapes of a scene. Previous methods that predict metric depth often work well only for a specific scene. In contrast, learning relative depth (information of being closer or further) can enjoy better generalization, with the price of failing to recover the accurate geometric shape of the scene. In this work, we propose a dataset and methods to tackle this dilemma, aiming to predict accurate depth up to an affine transfor"},"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":"2002.00569","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2020-02-03T05:38:33Z","cross_cats_sorted":[],"title_canon_sha256":"356b737d92dd85df82efb8ad621f07458a9cbc14606b3f9fa072a246486c1019","abstract_canon_sha256":"1899f6d797eb42ebc76b1d270e648421e2dcb4b2577cd6398e36bb75e5e53e04"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:51:06.410739Z","signature_b64":"iB34JR3/9CoJwuGmGga6JPmCxxOb6GTYA762NUblo0FBQy/Z67BG4xeF7LzZWIbjNeB9IhMIp6viNKvR8CDtDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74a2a16c9724a0c7351423dfd8320125022897cc9237705bc7bae73b58768dd3","last_reissued_at":"2026-07-05T00:51:06.410231Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:51:06.410231Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changming Sun, Chunhua Shen, Dou Renyin, Songcen Xu, Wei Yin, Xinlong Wang, Yifan Liu, Zhi Tian","submitted_at":"2020-02-03T05:38:33Z","abstract_excerpt":"We present a method for depth estimation with monocular images, which can predict high-quality depth on diverse scenes up to an affine transformation, thus preserving accurate shapes of a scene. Previous methods that predict metric depth often work well only for a specific scene. In contrast, learning relative depth (information of being closer or further) can enjoy better generalization, with the price of failing to recover the accurate geometric shape of the scene. In this work, we propose a dataset and methods to tackle this dilemma, aiming to predict accurate depth up to an affine transfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.00569","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/2002.00569/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":"2002.00569","created_at":"2026-07-05T00:51:06.410296+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.00569v3","created_at":"2026-07-05T00:51:06.410296+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.00569","created_at":"2026-07-05T00:51:06.410296+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSRKC3EXESQM","created_at":"2026-07-05T00:51:06.410296+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSRKC3EXESQMONIU","created_at":"2026-07-05T00:51:06.410296+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSRKC3EX","created_at":"2026-07-05T00:51:06.410296+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00386","citing_title":"{\\alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2501.02576","citing_title":"DepthMaster: Taming Diffusion Models for Monocular Depth Estimation","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11578","citing_title":"The Midas Touch for Metric Depth","ref_index":68,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05715","citing_title":"In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU","json":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU.json","graph_json":"https://pith.science/api/pith-number/OSRKC3EXESQMONIUEPP5QMQBEU/graph.json","events_json":"https://pith.science/api/pith-number/OSRKC3EXESQMONIUEPP5QMQBEU/events.json","paper":"https://pith.science/paper/OSRKC3EX"},"agent_actions":{"view_html":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU","download_json":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU.json","view_paper":"https://pith.science/paper/OSRKC3EX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.00569&json=true","fetch_graph":"https://pith.science/api/pith-number/OSRKC3EXESQMONIUEPP5QMQBEU/graph.json","fetch_events":"https://pith.science/api/pith-number/OSRKC3EXESQMONIUEPP5QMQBEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU/action/storage_attestation","attest_author":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU/action/author_attestation","sign_citation":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU/action/citation_signature","submit_replication":"https://pith.science/pith/OSRKC3EXESQMONIUEPP5QMQBEU/action/replication_record"}},"created_at":"2026-07-05T00:51:06.410296+00:00","updated_at":"2026-07-05T00:51:06.410296+00:00"}