{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3H56B3ENXWKXFV7COLSXYIXMBF","short_pith_number":"pith:3H56B3EN","schema_version":"1.0","canonical_sha256":"d9fbe0ec8dbd9572d7e272e57c22ec0942810d321f9d7edf087cd7f32c3ba8d2","source":{"kind":"arxiv","id":"2502.11534","version":1},"attestation_state":"computed","paper":{"title":"SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Adam Schmidt, Alexandre Banks, Haoying Zhou, Peter Kazanzides, Randy Moore, Septimiu E. Salcudean, Zijian Wu","submitted_at":"2025-02-17T08:04:53Z","abstract_excerpt":"Accurate and efficient surgical robotic tool pose estimation is of fundamental significance to downstream applications such as augmented reality (AR) in surgical training and learning-based autonomous manipulation. While significant advancements have been made in pose estimation for humans and animals, it is still a challenge in surgical robotics due to the scarcity of published data. The relatively large absolute error of the da Vinci end effector kinematics and arduous calibration procedure make calibrated kinematics data collection expensive. Driven by this limitation, we collected a datase"},"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":"2502.11534","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2025-02-17T08:04:53Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9a35e34e6f43f76a0769ac940ef4c00d2f5ca52bd11055ac9b403e45f56464cd","abstract_canon_sha256":"4a4fbb98b6dcbeb71e8a4099b8a6f84fe986de913babdebf0dda8523aef3cb9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:15:30.450015Z","signature_b64":"2tGKemt2VfAMz//XrUw9zuS+8K8sqDZf6sgi78EgjvlD0V5zus2hbOv5L/Kxpd3m6KBj/10SuDHeLHZpxZKzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9fbe0ec8dbd9572d7e272e57c22ec0942810d321f9d7edf087cd7f32c3ba8d2","last_reissued_at":"2026-07-05T10:15:30.449521Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:15:30.449521Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Adam Schmidt, Alexandre Banks, Haoying Zhou, Peter Kazanzides, Randy Moore, Septimiu E. Salcudean, Zijian Wu","submitted_at":"2025-02-17T08:04:53Z","abstract_excerpt":"Accurate and efficient surgical robotic tool pose estimation is of fundamental significance to downstream applications such as augmented reality (AR) in surgical training and learning-based autonomous manipulation. While significant advancements have been made in pose estimation for humans and animals, it is still a challenge in surgical robotics due to the scarcity of published data. The relatively large absolute error of the da Vinci end effector kinematics and arduous calibration procedure make calibrated kinematics data collection expensive. Driven by this limitation, we collected a datase"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11534","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/2502.11534/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":"2502.11534","created_at":"2026-07-05T10:15:30.449581+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.11534v1","created_at":"2026-07-05T10:15:30.449581+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11534","created_at":"2026-07-05T10:15:30.449581+00:00"},{"alias_kind":"pith_short_12","alias_value":"3H56B3ENXWKX","created_at":"2026-07-05T10:15:30.449581+00:00"},{"alias_kind":"pith_short_16","alias_value":"3H56B3ENXWKXFV7C","created_at":"2026-07-05T10:15:30.449581+00:00"},{"alias_kind":"pith_short_8","alias_value":"3H56B3EN","created_at":"2026-07-05T10:15:30.449581+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25598","citing_title":"SurfSurg6D: Geometry Consistent Dense Correspondence for Textureless Surgical Instrument Pose Estimation","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF","json":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF.json","graph_json":"https://pith.science/api/pith-number/3H56B3ENXWKXFV7COLSXYIXMBF/graph.json","events_json":"https://pith.science/api/pith-number/3H56B3ENXWKXFV7COLSXYIXMBF/events.json","paper":"https://pith.science/paper/3H56B3EN"},"agent_actions":{"view_html":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF","download_json":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF.json","view_paper":"https://pith.science/paper/3H56B3EN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.11534&json=true","fetch_graph":"https://pith.science/api/pith-number/3H56B3ENXWKXFV7COLSXYIXMBF/graph.json","fetch_events":"https://pith.science/api/pith-number/3H56B3ENXWKXFV7COLSXYIXMBF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF/action/storage_attestation","attest_author":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF/action/author_attestation","sign_citation":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF/action/citation_signature","submit_replication":"https://pith.science/pith/3H56B3ENXWKXFV7COLSXYIXMBF/action/replication_record"}},"created_at":"2026-07-05T10:15:30.449581+00:00","updated_at":"2026-07-05T10:15:30.449581+00:00"}