{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:NX2RZRZPBZXJXEGFIZYYCEDZNG","short_pith_number":"pith:NX2RZRZP","schema_version":"1.0","canonical_sha256":"6df51cc72f0e6e9b90c5467181107969984795e2da920b50d91ae87f6743c10f","source":{"kind":"arxiv","id":"2008.11098","version":1},"attestation_state":"computed","paper":{"title":"Improving Deep Stereo Network Generalization with Geometric Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Charles Loop, Deqing Sun, Jan Kautz, Jialiang Wang, Stan Birchfield, Varun Jampani","submitted_at":"2020-08-25T15:24:02Z","abstract_excerpt":"End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar. However, large datasets of diverse real-world scenes with dense ground truth are difficult to obtain and currently not publicly available to the research community. As a result, many algorithms rely on small real-world datasets of similar scenes or synthetic datasets, but end-to-end algorithms trained on such datasets often generalize poorly to different images that arise in real-world applications. As a step towards addressing this problem, w"},"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":"2008.11098","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-25T15:24:02Z","cross_cats_sorted":[],"title_canon_sha256":"b40955ef684dbf6d5648a17649d63ea58bf1ad405120aeb204307080d3efec92","abstract_canon_sha256":"88e24fd17b517079dbb98f9aec87ff13a23511500b2e229fbb6f93ad4087170b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:30:14.168804Z","signature_b64":"uZvq6GoeDEB8UWATOs95uvmrMm2mA5omjssHG+nRfoLLwmLYoUel2j0j9DYD5k+CiD78BfL9udmmZU2WCfhmBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6df51cc72f0e6e9b90c5467181107969984795e2da920b50d91ae87f6743c10f","last_reissued_at":"2026-07-05T01:30:14.168411Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:30:14.168411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Deep Stereo Network Generalization with Geometric Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Charles Loop, Deqing Sun, Jan Kautz, Jialiang Wang, Stan Birchfield, Varun Jampani","submitted_at":"2020-08-25T15:24:02Z","abstract_excerpt":"End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar. However, large datasets of diverse real-world scenes with dense ground truth are difficult to obtain and currently not publicly available to the research community. As a result, many algorithms rely on small real-world datasets of similar scenes or synthetic datasets, but end-to-end algorithms trained on such datasets often generalize poorly to different images that arise in real-world applications. As a step towards addressing this problem, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.11098","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/2008.11098/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":"2008.11098","created_at":"2026-07-05T01:30:14.168476+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.11098v1","created_at":"2026-07-05T01:30:14.168476+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.11098","created_at":"2026-07-05T01:30:14.168476+00:00"},{"alias_kind":"pith_short_12","alias_value":"NX2RZRZPBZXJ","created_at":"2026-07-05T01:30:14.168476+00:00"},{"alias_kind":"pith_short_16","alias_value":"NX2RZRZPBZXJXEGF","created_at":"2026-07-05T01:30:14.168476+00:00"},{"alias_kind":"pith_short_8","alias_value":"NX2RZRZP","created_at":"2026-07-05T01:30:14.168476+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/NX2RZRZPBZXJXEGFIZYYCEDZNG","json":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG.json","graph_json":"https://pith.science/api/pith-number/NX2RZRZPBZXJXEGFIZYYCEDZNG/graph.json","events_json":"https://pith.science/api/pith-number/NX2RZRZPBZXJXEGFIZYYCEDZNG/events.json","paper":"https://pith.science/paper/NX2RZRZP"},"agent_actions":{"view_html":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG","download_json":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG.json","view_paper":"https://pith.science/paper/NX2RZRZP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.11098&json=true","fetch_graph":"https://pith.science/api/pith-number/NX2RZRZPBZXJXEGFIZYYCEDZNG/graph.json","fetch_events":"https://pith.science/api/pith-number/NX2RZRZPBZXJXEGFIZYYCEDZNG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG/action/storage_attestation","attest_author":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG/action/author_attestation","sign_citation":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG/action/citation_signature","submit_replication":"https://pith.science/pith/NX2RZRZPBZXJXEGFIZYYCEDZNG/action/replication_record"}},"created_at":"2026-07-05T01:30:14.168476+00:00","updated_at":"2026-07-05T01:30:14.168476+00:00"}