{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PSWP4ETYUV4J4EV63IRUHQXW5P","short_pith_number":"pith:PSWP4ETY","schema_version":"1.0","canonical_sha256":"7cacfe1278a5789e12beda2343c2f6ebef678469705cf47c9cb0c1e1e7b3c637","source":{"kind":"arxiv","id":"2407.12239","version":3},"attestation_state":"computed","paper":{"title":"Motion and Structure from Event-based Normal Flow","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bangyan Liao, Delei Kong, Guillermo Gallego, Jinghang Li, Laurent Kneip, Peidong Liu, Yi Zhou, Zhongyang Ren","submitted_at":"2024-07-17T01:11:20Z","abstract_excerpt":"Recovering the camera motion and scene geometry from visual data is a fundamental problem in the field of computer vision. Its success in standard vision is attributed to the maturity of feature extraction, data association and multi-view geometry. The recent emergence of neuromorphic event-based cameras places great demands on approaches that use raw event data as input to solve this fundamental problem. Existing state-of-the-art solutions typically infer implicitly data association by iteratively reversing the event data generation process. However, the nonlinear nature of these methods limi"},"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":"2407.12239","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-17T01:11:20Z","cross_cats_sorted":[],"title_canon_sha256":"e8b8ad64fc6a860f19650a0aec8be3ca616d55a27df1955c8970fa6000136e29","abstract_canon_sha256":"3d8df0eeb3700f55923c6a2ee1dd76b3b092f882bca63e1899b9bef734e19007"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:46.105185Z","signature_b64":"qGrxSnfNymYjP5IfWRgE5EMaWJr6fq/xO6sqG5qYORenG/Nh9WHfX8dMUR8kyNYwre2/EhAIZtpAUAPsc3ZfCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cacfe1278a5789e12beda2343c2f6ebef678469705cf47c9cb0c1e1e7b3c637","last_reissued_at":"2026-07-05T09:17:46.104712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:46.104712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Motion and Structure from Event-based Normal Flow","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bangyan Liao, Delei Kong, Guillermo Gallego, Jinghang Li, Laurent Kneip, Peidong Liu, Yi Zhou, Zhongyang Ren","submitted_at":"2024-07-17T01:11:20Z","abstract_excerpt":"Recovering the camera motion and scene geometry from visual data is a fundamental problem in the field of computer vision. Its success in standard vision is attributed to the maturity of feature extraction, data association and multi-view geometry. The recent emergence of neuromorphic event-based cameras places great demands on approaches that use raw event data as input to solve this fundamental problem. Existing state-of-the-art solutions typically infer implicitly data association by iteratively reversing the event data generation process. However, the nonlinear nature of these methods limi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.12239","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/2407.12239/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":"2407.12239","created_at":"2026-07-05T09:17:46.104770+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.12239v3","created_at":"2026-07-05T09:17:46.104770+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.12239","created_at":"2026-07-05T09:17:46.104770+00:00"},{"alias_kind":"pith_short_12","alias_value":"PSWP4ETYUV4J","created_at":"2026-07-05T09:17:46.104770+00:00"},{"alias_kind":"pith_short_16","alias_value":"PSWP4ETYUV4J4EV6","created_at":"2026-07-05T09:17:46.104770+00:00"},{"alias_kind":"pith_short_8","alias_value":"PSWP4ETY","created_at":"2026-07-05T09:17:46.104770+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11284","citing_title":"Learning Normal Flow Directly From Event Neighborhoods","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P","json":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P.json","graph_json":"https://pith.science/api/pith-number/PSWP4ETYUV4J4EV63IRUHQXW5P/graph.json","events_json":"https://pith.science/api/pith-number/PSWP4ETYUV4J4EV63IRUHQXW5P/events.json","paper":"https://pith.science/paper/PSWP4ETY"},"agent_actions":{"view_html":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P","download_json":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P.json","view_paper":"https://pith.science/paper/PSWP4ETY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.12239&json=true","fetch_graph":"https://pith.science/api/pith-number/PSWP4ETYUV4J4EV63IRUHQXW5P/graph.json","fetch_events":"https://pith.science/api/pith-number/PSWP4ETYUV4J4EV63IRUHQXW5P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P/action/storage_attestation","attest_author":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P/action/author_attestation","sign_citation":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P/action/citation_signature","submit_replication":"https://pith.science/pith/PSWP4ETYUV4J4EV63IRUHQXW5P/action/replication_record"}},"created_at":"2026-07-05T09:17:46.104770+00:00","updated_at":"2026-07-05T09:17:46.104770+00:00"}