{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HDYN2PGYH2QEZEGYHJY44BWRXD","short_pith_number":"pith:HDYN2PGY","schema_version":"1.0","canonical_sha256":"38f0dd3cd83ea04c90d83a71ce06d1b8f08d567a57509aa46c3f9c6f947b22e2","source":{"kind":"arxiv","id":"2211.13398","version":3},"attestation_state":"computed","paper":{"title":"CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Hongkai Xiong, Jin Liu, Weiming Wang, Wenhao He, Yang You","submitted_at":"2022-11-24T03:27:00Z","abstract_excerpt":"Object pose estimation constitutes a critical area within the domain of 3D vision. While contemporary state-of-the-art methods that leverage real-world pose annotations have demonstrated commendable performance, the procurement of such real training data incurs substantial costs. This paper focuses on a specific setting wherein only 3D CAD models are utilized as a priori knowledge, devoid of any background or clutter information. We introduce a novel method, CPPF++, designed for sim-to-real pose estimation. This method builds upon the foundational point-pair voting scheme of CPPF, reformulatin"},"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":"2211.13398","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-24T03:27:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0ece8e13121d40156182773e9522cd796bb2e769112369590f32577d57faa22f","abstract_canon_sha256":"d1cdaf155c3ad535376fa61c3f880dfd1fbb366ed366413081b4010b69113649"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:57.949297Z","signature_b64":"4lag6aOHvNE6hz1T404bpslbIK5sNG9fpaWjXpVj/WNbL5+XJEDsYrDE5H2dmBkh2g0ggbQXo46J4Qyz4soPDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38f0dd3cd83ea04c90d83a71ce06d1b8f08d567a57509aa46c3f9c6f947b22e2","last_reissued_at":"2026-07-05T08:01:57.948895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:57.948895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Hongkai Xiong, Jin Liu, Weiming Wang, Wenhao He, Yang You","submitted_at":"2022-11-24T03:27:00Z","abstract_excerpt":"Object pose estimation constitutes a critical area within the domain of 3D vision. While contemporary state-of-the-art methods that leverage real-world pose annotations have demonstrated commendable performance, the procurement of such real training data incurs substantial costs. This paper focuses on a specific setting wherein only 3D CAD models are utilized as a priori knowledge, devoid of any background or clutter information. We introduce a novel method, CPPF++, designed for sim-to-real pose estimation. This method builds upon the foundational point-pair voting scheme of CPPF, reformulatin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13398","kind":"arxiv","version":3},"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/2211.13398/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":"2211.13398","created_at":"2026-07-05T08:01:57.948957+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.13398v3","created_at":"2026-07-05T08:01:57.948957+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13398","created_at":"2026-07-05T08:01:57.948957+00:00"},{"alias_kind":"pith_short_12","alias_value":"HDYN2PGYH2QE","created_at":"2026-07-05T08:01:57.948957+00:00"},{"alias_kind":"pith_short_16","alias_value":"HDYN2PGYH2QEZEGY","created_at":"2026-07-05T08:01:57.948957+00:00"},{"alias_kind":"pith_short_8","alias_value":"HDYN2PGY","created_at":"2026-07-05T08:01:57.948957+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/HDYN2PGYH2QEZEGYHJY44BWRXD","json":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD.json","graph_json":"https://pith.science/api/pith-number/HDYN2PGYH2QEZEGYHJY44BWRXD/graph.json","events_json":"https://pith.science/api/pith-number/HDYN2PGYH2QEZEGYHJY44BWRXD/events.json","paper":"https://pith.science/paper/HDYN2PGY"},"agent_actions":{"view_html":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD","download_json":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD.json","view_paper":"https://pith.science/paper/HDYN2PGY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.13398&json=true","fetch_graph":"https://pith.science/api/pith-number/HDYN2PGYH2QEZEGYHJY44BWRXD/graph.json","fetch_events":"https://pith.science/api/pith-number/HDYN2PGYH2QEZEGYHJY44BWRXD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD/action/storage_attestation","attest_author":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD/action/author_attestation","sign_citation":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD/action/citation_signature","submit_replication":"https://pith.science/pith/HDYN2PGYH2QEZEGYHJY44BWRXD/action/replication_record"}},"created_at":"2026-07-05T08:01:57.948957+00:00","updated_at":"2026-07-05T08:01:57.948957+00:00"}