{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XUDVWKBUIP7MMSGBXMBSRGXI62","short_pith_number":"pith:XUDVWKBU","schema_version":"1.0","canonical_sha256":"bd075b283443fec648c1bb03289ae8f68fcaf76ec54a93b02e94f292ce588746","source":{"kind":"arxiv","id":"2607.15058","version":1},"attestation_state":"computed","paper":{"title":"SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Holger Voos, Javier Civera, Jose Luis Sanchez-Lopez, Miguel Fernandez-Cortizas, Saad Ejaz","submitted_at":"2026-07-16T14:32:34Z","abstract_excerpt":"CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are appearance-driven and degrade under occlusion or sim-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD Alignment), a weakly-supervised framework for zero-shot CAD alignment with two key contributions. First, SUFLECA sca"},"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":"2607.15058","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-16T14:32:34Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"23f5d1bbc81ce25b5a09912abf0882e9f00bc2502cd2bd8363dac69819c63a9a","abstract_canon_sha256":"47df7ee4cd86fd1faadcc523bc1835b8adcb680d9fc66ad4d60345926139b25e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T01:22:07.385832Z","signature_b64":"H/akE6CMy87W6CPLijV15oq5ZfGWIKh9p1ovDgSPTZlRxgwcEzPEZfMm2JI0M6WZ683WsD5/6JWSxH097BEJAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd075b283443fec648c1bb03289ae8f68fcaf76ec54a93b02e94f292ce588746","last_reissued_at":"2026-07-17T01:22:07.384983Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T01:22:07.384983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Holger Voos, Javier Civera, Jose Luis Sanchez-Lopez, Miguel Fernandez-Cortizas, Saad Ejaz","submitted_at":"2026-07-16T14:32:34Z","abstract_excerpt":"CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are appearance-driven and degrade under occlusion or sim-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD Alignment), a weakly-supervised framework for zero-shot CAD alignment with two key contributions. First, SUFLECA sca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15058","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/2607.15058/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":"2607.15058","created_at":"2026-07-17T01:22:07.385425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15058v1","created_at":"2026-07-17T01:22:07.385425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15058","created_at":"2026-07-17T01:22:07.385425+00:00"},{"alias_kind":"pith_short_12","alias_value":"XUDVWKBUIP7M","created_at":"2026-07-17T01:22:07.385425+00:00"},{"alias_kind":"pith_short_16","alias_value":"XUDVWKBUIP7MMSGB","created_at":"2026-07-17T01:22:07.385425+00:00"},{"alias_kind":"pith_short_8","alias_value":"XUDVWKBU","created_at":"2026-07-17T01:22:07.385425+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/XUDVWKBUIP7MMSGBXMBSRGXI62","json":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62.json","graph_json":"https://pith.science/api/pith-number/XUDVWKBUIP7MMSGBXMBSRGXI62/graph.json","events_json":"https://pith.science/api/pith-number/XUDVWKBUIP7MMSGBXMBSRGXI62/events.json","paper":"https://pith.science/paper/XUDVWKBU"},"agent_actions":{"view_html":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62","download_json":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62.json","view_paper":"https://pith.science/paper/XUDVWKBU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15058&json=true","fetch_graph":"https://pith.science/api/pith-number/XUDVWKBUIP7MMSGBXMBSRGXI62/graph.json","fetch_events":"https://pith.science/api/pith-number/XUDVWKBUIP7MMSGBXMBSRGXI62/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62/action/storage_attestation","attest_author":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62/action/author_attestation","sign_citation":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62/action/citation_signature","submit_replication":"https://pith.science/pith/XUDVWKBUIP7MMSGBXMBSRGXI62/action/replication_record"}},"created_at":"2026-07-17T01:22:07.385425+00:00","updated_at":"2026-07-17T01:22:07.385425+00:00"}