{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VQTEVOP6AJKAUSR4UVN36T4OVE","short_pith_number":"pith:VQTEVOP6","schema_version":"1.0","canonical_sha256":"ac264ab9fe02540a4a3ca55bbf4f8ea9057ba5fbbeaca06d511a9058b50c53c0","source":{"kind":"arxiv","id":"2109.07547","version":1},"attestation_state":"computed","paper":{"title":"RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jia Deng, Lahav Lipson, Zachary Teed","submitted_at":"2021-09-15T19:27:31Z","abstract_excerpt":"We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT. We introduce multi-level convolutional GRUs, which more efficiently propagate information across the image. A modified version of RAFT-Stereo can perform accurate real-time inference. RAFT-stereo ranks first on the Middlebury leaderboard, outperforming the next best method on 1px error by 29% and outperforms all published work on the ETH3D two-view stereo benchmark. Code is available at https://github.com/princeton-vl/RAFT-Stereo."},"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":"2109.07547","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-15T19:27:31Z","cross_cats_sorted":[],"title_canon_sha256":"62a32f7d84a309576fc313eedd2852873b0b40aff816fbc0339a62b4af350f40","abstract_canon_sha256":"e1e5a374dc4267319336b8c1af67d1413069833851363b98fcb1d7121cc214a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:14:58.228158Z","signature_b64":"ofk0g30rnR0TROeCKDqDbhic7dLS2lfOaoaIjmqIn0LYmZ/tco02xBM3HIjmOGatK3Y8CxqdcmahcXVARoU3CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac264ab9fe02540a4a3ca55bbf4f8ea9057ba5fbbeaca06d511a9058b50c53c0","last_reissued_at":"2026-07-05T03:14:58.227828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:14:58.227828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jia Deng, Lahav Lipson, Zachary Teed","submitted_at":"2021-09-15T19:27:31Z","abstract_excerpt":"We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT. We introduce multi-level convolutional GRUs, which more efficiently propagate information across the image. A modified version of RAFT-Stereo can perform accurate real-time inference. RAFT-stereo ranks first on the Middlebury leaderboard, outperforming the next best method on 1px error by 29% and outperforms all published work on the ETH3D two-view stereo benchmark. Code is available at https://github.com/princeton-vl/RAFT-Stereo."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.07547","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/2109.07547/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":"2109.07547","created_at":"2026-07-05T03:14:58.227883+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.07547v1","created_at":"2026-07-05T03:14:58.227883+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.07547","created_at":"2026-07-05T03:14:58.227883+00:00"},{"alias_kind":"pith_short_12","alias_value":"VQTEVOP6AJKA","created_at":"2026-07-05T03:14:58.227883+00:00"},{"alias_kind":"pith_short_16","alias_value":"VQTEVOP6AJKAUSR4","created_at":"2026-07-05T03:14:58.227883+00:00"},{"alias_kind":"pith_short_8","alias_value":"VQTEVOP6","created_at":"2026-07-05T03:14:58.227883+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24457","citing_title":"Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10364","citing_title":"Benchmarking stereo reconstruction for 3D printable Martian terrain models","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09989","citing_title":"StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception","ref_index":84,"is_internal_anchor":false},{"citing_arxiv_id":"2511.16555","citing_title":"Lite Any Stereo: Efficient Zero-Shot Stereo Matching","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09989","citing_title":"StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception","ref_index":69,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE","json":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE.json","graph_json":"https://pith.science/api/pith-number/VQTEVOP6AJKAUSR4UVN36T4OVE/graph.json","events_json":"https://pith.science/api/pith-number/VQTEVOP6AJKAUSR4UVN36T4OVE/events.json","paper":"https://pith.science/paper/VQTEVOP6"},"agent_actions":{"view_html":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE","download_json":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE.json","view_paper":"https://pith.science/paper/VQTEVOP6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.07547&json=true","fetch_graph":"https://pith.science/api/pith-number/VQTEVOP6AJKAUSR4UVN36T4OVE/graph.json","fetch_events":"https://pith.science/api/pith-number/VQTEVOP6AJKAUSR4UVN36T4OVE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE/action/storage_attestation","attest_author":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE/action/author_attestation","sign_citation":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE/action/citation_signature","submit_replication":"https://pith.science/pith/VQTEVOP6AJKAUSR4UVN36T4OVE/action/replication_record"}},"created_at":"2026-07-05T03:14:58.227883+00:00","updated_at":"2026-07-05T03:14:58.227883+00:00"}