{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:I5CUG4Y3R5SMGTOFSDT4NLZWDP","short_pith_number":"pith:I5CUG4Y3","schema_version":"1.0","canonical_sha256":"474543731b8f64c34dc590e7c6af361bfa5d892ef3a342118523499dd6326c7c","source":{"kind":"arxiv","id":"2506.04619","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning Reforms Image Matching: A Survey and Outlook","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiayi Ma, Kaining Zhang, Linfeng Tang, Shihua Zhang, Xingyu Jiang, Yifan Lu, Yuxin Deng, Zizhuo Li","submitted_at":"2025-06-05T04:25:22Z","abstract_excerpt":"Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in computer vision and underpins a wide range of applications, including visual localization, 3D reconstruction, and simultaneous localization and mapping (SLAM). Traditional pipelines composed of ``detector-descriptor, feature matcher, outlier filter, and geometric estimator'' falter in challenging scenarios. Recent deep-learning advances have significantly boosted both robustness and accuracy. This survey adopts a unique perspective by comprehensively"},"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":"2506.04619","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-05T04:25:22Z","cross_cats_sorted":[],"title_canon_sha256":"af2a84abb94da38a8fedfbb4c4470bff40fb41f285b5bd5aaff2093391616a41","abstract_canon_sha256":"52971754f985fb05b0fd62c6790423821a63b44368ccf9fd4be01089ff31fe98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:17.960154Z","signature_b64":"bBXeCfSbAIwrMcD8D0TlItb3hb5/A6tUalpwN/Wz/Wgjq59mRWWBnZO6qdoIFawpvD1n+zhcaOvrBRsKL0ZAAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"474543731b8f64c34dc590e7c6af361bfa5d892ef3a342118523499dd6326c7c","last_reissued_at":"2026-07-05T11:16:17.959635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:17.959635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning Reforms Image Matching: A Survey and Outlook","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiayi Ma, Kaining Zhang, Linfeng Tang, Shihua Zhang, Xingyu Jiang, Yifan Lu, Yuxin Deng, Zizhuo Li","submitted_at":"2025-06-05T04:25:22Z","abstract_excerpt":"Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in computer vision and underpins a wide range of applications, including visual localization, 3D reconstruction, and simultaneous localization and mapping (SLAM). Traditional pipelines composed of ``detector-descriptor, feature matcher, outlier filter, and geometric estimator'' falter in challenging scenarios. Recent deep-learning advances have significantly boosted both robustness and accuracy. This survey adopts a unique perspective by comprehensively"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.04619","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/2506.04619/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":"2506.04619","created_at":"2026-07-05T11:16:17.959711+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.04619v1","created_at":"2026-07-05T11:16:17.959711+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.04619","created_at":"2026-07-05T11:16:17.959711+00:00"},{"alias_kind":"pith_short_12","alias_value":"I5CUG4Y3R5SM","created_at":"2026-07-05T11:16:17.959711+00:00"},{"alias_kind":"pith_short_16","alias_value":"I5CUG4Y3R5SMGTOF","created_at":"2026-07-05T11:16:17.959711+00:00"},{"alias_kind":"pith_short_8","alias_value":"I5CUG4Y3","created_at":"2026-07-05T11:16:17.959711+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10081","citing_title":"MatRes: Zero-Shot Test-Time Model Adaptation for Simultaneous Matching and Restoration","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP","json":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP.json","graph_json":"https://pith.science/api/pith-number/I5CUG4Y3R5SMGTOFSDT4NLZWDP/graph.json","events_json":"https://pith.science/api/pith-number/I5CUG4Y3R5SMGTOFSDT4NLZWDP/events.json","paper":"https://pith.science/paper/I5CUG4Y3"},"agent_actions":{"view_html":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP","download_json":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP.json","view_paper":"https://pith.science/paper/I5CUG4Y3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.04619&json=true","fetch_graph":"https://pith.science/api/pith-number/I5CUG4Y3R5SMGTOFSDT4NLZWDP/graph.json","fetch_events":"https://pith.science/api/pith-number/I5CUG4Y3R5SMGTOFSDT4NLZWDP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP/action/storage_attestation","attest_author":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP/action/author_attestation","sign_citation":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP/action/citation_signature","submit_replication":"https://pith.science/pith/I5CUG4Y3R5SMGTOFSDT4NLZWDP/action/replication_record"}},"created_at":"2026-07-05T11:16:17.959711+00:00","updated_at":"2026-07-05T11:16:17.959711+00:00"}