{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:NX2RZRZPBZXJXEGFIZYYCEDZNG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"88e24fd17b517079dbb98f9aec87ff13a23511500b2e229fbb6f93ad4087170b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-25T15:24:02Z","title_canon_sha256":"b40955ef684dbf6d5648a17649d63ea58bf1ad405120aeb204307080d3efec92"},"schema_version":"1.0","source":{"id":"2008.11098","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2008.11098","created_at":"2026-07-05T01:30:14Z"},{"alias_kind":"arxiv_version","alias_value":"2008.11098v1","created_at":"2026-07-05T01:30:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.11098","created_at":"2026-07-05T01:30:14Z"},{"alias_kind":"pith_short_12","alias_value":"NX2RZRZPBZXJ","created_at":"2026-07-05T01:30:14Z"},{"alias_kind":"pith_short_16","alias_value":"NX2RZRZPBZXJXEGF","created_at":"2026-07-05T01:30:14Z"},{"alias_kind":"pith_short_8","alias_value":"NX2RZRZP","created_at":"2026-07-05T01:30:14Z"}],"graph_snapshots":[{"event_id":"sha256:5c0d0935807536273e497ed296cfba6e0b51d0b7ff0ff2069ce5f5e340890e27","target":"graph","created_at":"2026-07-05T01:30:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2008.11098/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Charles Loop, Deqing Sun, Jan Kautz, Jialiang Wang, Stan Birchfield, Varun Jampani","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-25T15:24:02Z","title":"Improving Deep Stereo Network Generalization with Geometric Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.11098","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e69edfef9da1802cc0875a4689ce5e483533ba3259b06bdbc783f1a5485a531a","target":"record","created_at":"2026-07-05T01:30:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"88e24fd17b517079dbb98f9aec87ff13a23511500b2e229fbb6f93ad4087170b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-08-25T15:24:02Z","title_canon_sha256":"b40955ef684dbf6d5648a17649d63ea58bf1ad405120aeb204307080d3efec92"},"schema_version":"1.0","source":{"id":"2008.11098","kind":"arxiv","version":1}},"canonical_sha256":"6df51cc72f0e6e9b90c5467181107969984795e2da920b50d91ae87f6743c10f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6df51cc72f0e6e9b90c5467181107969984795e2da920b50d91ae87f6743c10f","first_computed_at":"2026-07-05T01:30:14.168411Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:30:14.168411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uZvq6GoeDEB8UWATOs95uvmrMm2mA5omjssHG+nRfoLLwmLYoUel2j0j9DYD5k+CiD78BfL9udmmZU2WCfhmBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:30:14.168804Z","signed_message":"canonical_sha256_bytes"},"source_id":"2008.11098","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e69edfef9da1802cc0875a4689ce5e483533ba3259b06bdbc783f1a5485a531a","sha256:5c0d0935807536273e497ed296cfba6e0b51d0b7ff0ff2069ce5f5e340890e27"],"state_sha256":"be1053b3eea3a1e822f1a4b84274e0b956bdca158aa0a0720e50913a0b209527"}