{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WVSHJHYXJRHM5KE3SLMA6O6PVP","short_pith_number":"pith:WVSHJHYX","schema_version":"1.0","canonical_sha256":"b564749f174c4ecea89b92d80f3bcfabed6a40f653c8a34159fcc144d638674d","source":{"kind":"arxiv","id":"2504.14699","version":1},"attestation_state":"computed","paper":{"title":"IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aidana Massalimova, Christoph J. Laux, Lilian Calvet, Mazda Farshad, Philipp F\\\"urnstahl, Ruyi Zha, Sascha Jecklin","submitted_at":"2025-04-20T18:28:13Z","abstract_excerpt":"Spine surgery is a high-risk intervention demanding precise execution, often supported by image-based navigation systems. Recently, supervised learning approaches have gained attention for reconstructing 3D spinal anatomy from sparse fluoroscopic data, significantly reducing reliance on radiation-intensive 3D imaging systems. However, these methods typically require large amounts of annotated training data and may struggle to generalize across varying patient anatomies or imaging conditions. Instance-learning approaches like Gaussian splatting could offer an alternative by avoiding extensive a"},"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":"2504.14699","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-20T18:28:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4949e5690de8578e428b85b48c8a53bac3f3c7f9e1f5288b96f1aab1cc17e3a5","abstract_canon_sha256":"e15ce33389085d83d77711b200a482d03cc545e69504564a966cc168d541a429"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:49.279012Z","signature_b64":"HfLypyAyScC3gBFu5vpZAfMK2TaLTw18nwTxHHUPLDEuoHYgiJXjFtIz9cmcIA0DCx8uC8jUnzikol7HLPwvAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b564749f174c4ecea89b92d80f3bcfabed6a40f653c8a34159fcc144d638674d","last_reissued_at":"2026-07-05T10:51:49.278525Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:49.278525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aidana Massalimova, Christoph J. Laux, Lilian Calvet, Mazda Farshad, Philipp F\\\"urnstahl, Ruyi Zha, Sascha Jecklin","submitted_at":"2025-04-20T18:28:13Z","abstract_excerpt":"Spine surgery is a high-risk intervention demanding precise execution, often supported by image-based navigation systems. Recently, supervised learning approaches have gained attention for reconstructing 3D spinal anatomy from sparse fluoroscopic data, significantly reducing reliance on radiation-intensive 3D imaging systems. However, these methods typically require large amounts of annotated training data and may struggle to generalize across varying patient anatomies or imaging conditions. Instance-learning approaches like Gaussian splatting could offer an alternative by avoiding extensive a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14699","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/2504.14699/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":"2504.14699","created_at":"2026-07-05T10:51:49.278582+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14699v1","created_at":"2026-07-05T10:51:49.278582+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14699","created_at":"2026-07-05T10:51:49.278582+00:00"},{"alias_kind":"pith_short_12","alias_value":"WVSHJHYXJRHM","created_at":"2026-07-05T10:51:49.278582+00:00"},{"alias_kind":"pith_short_16","alias_value":"WVSHJHYXJRHM5KE3","created_at":"2026-07-05T10:51:49.278582+00:00"},{"alias_kind":"pith_short_8","alias_value":"WVSHJHYX","created_at":"2026-07-05T10:51:49.278582+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP","json":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP.json","graph_json":"https://pith.science/api/pith-number/WVSHJHYXJRHM5KE3SLMA6O6PVP/graph.json","events_json":"https://pith.science/api/pith-number/WVSHJHYXJRHM5KE3SLMA6O6PVP/events.json","paper":"https://pith.science/paper/WVSHJHYX"},"agent_actions":{"view_html":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP","download_json":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP.json","view_paper":"https://pith.science/paper/WVSHJHYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14699&json=true","fetch_graph":"https://pith.science/api/pith-number/WVSHJHYXJRHM5KE3SLMA6O6PVP/graph.json","fetch_events":"https://pith.science/api/pith-number/WVSHJHYXJRHM5KE3SLMA6O6PVP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP/action/storage_attestation","attest_author":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP/action/author_attestation","sign_citation":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP/action/citation_signature","submit_replication":"https://pith.science/pith/WVSHJHYXJRHM5KE3SLMA6O6PVP/action/replication_record"}},"created_at":"2026-07-05T10:51:49.278582+00:00","updated_at":"2026-07-05T10:51:49.278582+00:00"}