{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AQSOZXVAP6C6YPQAYSIWL24VMW","short_pith_number":"pith:AQSOZXVA","schema_version":"1.0","canonical_sha256":"0424ecdea07f85ec3e00c49165eb95659ddf267c9e6e5ca89b6cd3fa3bbc943d","source":{"kind":"arxiv","id":"2503.22963","version":1},"attestation_state":"computed","paper":{"title":"SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fuling Lin, Peiyu Chen, Peng Lu, Weipeng Guan","submitted_at":"2025-03-29T03:58:15Z","abstract_excerpt":"Event cameras asynchronously output low-latency event streams, promising for state estimation in high-speed motion and challenging lighting conditions. As opposed to frame-based cameras, the motion-dependent nature of event cameras presents persistent challenges in achieving robust event feature detection and matching. In recent years, learning-based approaches have demonstrated superior robustness over traditional handcrafted methods in feature detection and matching, particularly under aggressive motion and HDR scenarios. In this paper, we propose SuperEIO, a novel framework that leverages t"},"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":"2503.22963","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-03-29T03:58:15Z","cross_cats_sorted":[],"title_canon_sha256":"b67d2c1a25ce87d186def38627e5d76090ec4973e2a250f43bae2c17ea286437","abstract_canon_sha256":"f588f616bf92e998036c5a2040cfa8035f2418255bd54f0cda4bebed5e4341fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:41:28.240659Z","signature_b64":"Hk9Q43x1fFoA2EuZs8Rg8l/4gBqLQtAvBzpwRDZzQCgOuBPGl3lHdvEqRalsnNdHNocRzsWbpza42uw4sa+nDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0424ecdea07f85ec3e00c49165eb95659ddf267c9e6e5ca89b6cd3fa3bbc943d","last_reissued_at":"2026-07-05T10:41:28.240153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:41:28.240153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fuling Lin, Peiyu Chen, Peng Lu, Weipeng Guan","submitted_at":"2025-03-29T03:58:15Z","abstract_excerpt":"Event cameras asynchronously output low-latency event streams, promising for state estimation in high-speed motion and challenging lighting conditions. As opposed to frame-based cameras, the motion-dependent nature of event cameras presents persistent challenges in achieving robust event feature detection and matching. In recent years, learning-based approaches have demonstrated superior robustness over traditional handcrafted methods in feature detection and matching, particularly under aggressive motion and HDR scenarios. In this paper, we propose SuperEIO, a novel framework that leverages t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.22963","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/2503.22963/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":"2503.22963","created_at":"2026-07-05T10:41:28.240207+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.22963v1","created_at":"2026-07-05T10:41:28.240207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.22963","created_at":"2026-07-05T10:41:28.240207+00:00"},{"alias_kind":"pith_short_12","alias_value":"AQSOZXVAP6C6","created_at":"2026-07-05T10:41:28.240207+00:00"},{"alias_kind":"pith_short_16","alias_value":"AQSOZXVAP6C6YPQA","created_at":"2026-07-05T10:41:28.240207+00:00"},{"alias_kind":"pith_short_8","alias_value":"AQSOZXVA","created_at":"2026-07-05T10:41:28.240207+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.14500","citing_title":"Motion Segmentation and Egomotion Estimation from Event-Based Normal Flow","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW","json":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW.json","graph_json":"https://pith.science/api/pith-number/AQSOZXVAP6C6YPQAYSIWL24VMW/graph.json","events_json":"https://pith.science/api/pith-number/AQSOZXVAP6C6YPQAYSIWL24VMW/events.json","paper":"https://pith.science/paper/AQSOZXVA"},"agent_actions":{"view_html":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW","download_json":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW.json","view_paper":"https://pith.science/paper/AQSOZXVA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.22963&json=true","fetch_graph":"https://pith.science/api/pith-number/AQSOZXVAP6C6YPQAYSIWL24VMW/graph.json","fetch_events":"https://pith.science/api/pith-number/AQSOZXVAP6C6YPQAYSIWL24VMW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW/action/storage_attestation","attest_author":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW/action/author_attestation","sign_citation":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW/action/citation_signature","submit_replication":"https://pith.science/pith/AQSOZXVAP6C6YPQAYSIWL24VMW/action/replication_record"}},"created_at":"2026-07-05T10:41:28.240207+00:00","updated_at":"2026-07-05T10:41:28.240207+00:00"}