{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QUMTYVERA6COZF2X3BV7ODRF5M","short_pith_number":"pith:QUMTYVER","schema_version":"1.0","canonical_sha256":"85193c54910784ec9757d86bf70e25eb124c44f256fae0a6736badc0690bdac0","source":{"kind":"arxiv","id":"2412.08421","version":2},"attestation_state":"computed","paper":{"title":"PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong-Ming Yan, Jie Jiang, Weize Quan, Yingmei Wei, Yi Zhong","submitted_at":"2024-12-11T14:37:21Z","abstract_excerpt":"Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances in point cloud analysis techniques, feature extraction methods are still confronted with apparent limitations. The sparse sampling of point clouds, used as inputs in most methods, often results in a certain loss of global structure information. Meanwhile, traditional local feature extraction methods usually struggle to capture the intricate geometric details. To overcome these drawbacks, we intro"},"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":"2412.08421","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-11T14:37:21Z","cross_cats_sorted":[],"title_canon_sha256":"57b93626bcfd3f7c915c53e3eeae71c320d1a19452f13b5c4ecd42805cdff6c5","abstract_canon_sha256":"e0bdd53a8680e1f2542fc26bfac6794a2e49c61dc418e18ac249e4b5b4eb7732"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:14.101679Z","signature_b64":"VoyGqQ7bpcfB1wPpGWaU2M34UelK6JHqrxtEVq7ttXNo9OwZ7YZOEYhVJclwGk5SPIEZ4R+jKS9hY6RA7lFxAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85193c54910784ec9757d86bf70e25eb124c44f256fae0a6736badc0690bdac0","last_reissued_at":"2026-07-05T09:49:14.101200Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:14.101200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong-Ming Yan, Jie Jiang, Weize Quan, Yingmei Wei, Yi Zhong","submitted_at":"2024-12-11T14:37:21Z","abstract_excerpt":"Point cloud completion aims to reconstruct the complete 3D shape from incomplete point clouds, and it is crucial for tasks such as 3D object detection and segmentation. Despite the continuous advances in point cloud analysis techniques, feature extraction methods are still confronted with apparent limitations. The sparse sampling of point clouds, used as inputs in most methods, often results in a certain loss of global structure information. Meanwhile, traditional local feature extraction methods usually struggle to capture the intricate geometric details. To overcome these drawbacks, we intro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08421","kind":"arxiv","version":2},"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/2412.08421/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":"2412.08421","created_at":"2026-07-05T09:49:14.101263+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.08421v2","created_at":"2026-07-05T09:49:14.101263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08421","created_at":"2026-07-05T09:49:14.101263+00:00"},{"alias_kind":"pith_short_12","alias_value":"QUMTYVERA6CO","created_at":"2026-07-05T09:49:14.101263+00:00"},{"alias_kind":"pith_short_16","alias_value":"QUMTYVERA6COZF2X","created_at":"2026-07-05T09:49:14.101263+00:00"},{"alias_kind":"pith_short_8","alias_value":"QUMTYVER","created_at":"2026-07-05T09:49:14.101263+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.08421","citing_title":"PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion","ref_index":39,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M","json":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M.json","graph_json":"https://pith.science/api/pith-number/QUMTYVERA6COZF2X3BV7ODRF5M/graph.json","events_json":"https://pith.science/api/pith-number/QUMTYVERA6COZF2X3BV7ODRF5M/events.json","paper":"https://pith.science/paper/QUMTYVER"},"agent_actions":{"view_html":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M","download_json":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M.json","view_paper":"https://pith.science/paper/QUMTYVER","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.08421&json=true","fetch_graph":"https://pith.science/api/pith-number/QUMTYVERA6COZF2X3BV7ODRF5M/graph.json","fetch_events":"https://pith.science/api/pith-number/QUMTYVERA6COZF2X3BV7ODRF5M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M/action/storage_attestation","attest_author":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M/action/author_attestation","sign_citation":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M/action/citation_signature","submit_replication":"https://pith.science/pith/QUMTYVERA6COZF2X3BV7ODRF5M/action/replication_record"}},"created_at":"2026-07-05T09:49:14.101263+00:00","updated_at":"2026-07-05T09:49:14.101263+00:00"}