{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IBUXFM5GRON6E3IBZXXHCEQLKA","short_pith_number":"pith:IBUXFM5G","schema_version":"1.0","canonical_sha256":"406972b3a68b9be26d01cdee71120b502232fc174af5d4041eb2d2b1b96d90a1","source":{"kind":"arxiv","id":"2405.16085","version":1},"attestation_state":"computed","paper":{"title":"Deep-PE: A Learning-Based Pose Evaluator for Point Cloud Registration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changhe Tu, Chongjian Wang, Junjie Gao, Shiqing Xin, Shuangmin Chen, Wenping Wang, Zhongjun Ding","submitted_at":"2024-05-25T06:32:32Z","abstract_excerpt":"In the realm of point cloud registration, the most prevalent pose evaluation approaches are statistics-based, identifying the optimal transformation by maximizing the number of consistent correspondences. However, registration recall decreases significantly when point clouds exhibit a low overlap rate, despite efforts in designing feature descriptors and establishing correspondences. In this paper, we introduce Deep-PE, a lightweight, learning-based pose evaluator designed to enhance the accuracy of pose selection, especially in challenging point cloud scenarios with low overlap. Our network i"},"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":"2405.16085","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-25T06:32:32Z","cross_cats_sorted":[],"title_canon_sha256":"39efe80d3b303f49e56169466ce3aa3bfa194b72b185adf53769e7e79004d8fe","abstract_canon_sha256":"760e78f10fb0a22fe979769d19b0c58affdacaa92783d4142718f60197c22b67"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:25.602507Z","signature_b64":"h4Ctjd77DPEXxKRJzU42vetnD4xkOyQWEEEi1iEiP1/engjqyJe3mKwsvnNrv5GKvQMKv8QtDpW4pgy4K9v6DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"406972b3a68b9be26d01cdee71120b502232fc174af5d4041eb2d2b1b96d90a1","last_reissued_at":"2026-07-05T08:23:25.601977Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:25.601977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep-PE: A Learning-Based Pose Evaluator for Point Cloud Registration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changhe Tu, Chongjian Wang, Junjie Gao, Shiqing Xin, Shuangmin Chen, Wenping Wang, Zhongjun Ding","submitted_at":"2024-05-25T06:32:32Z","abstract_excerpt":"In the realm of point cloud registration, the most prevalent pose evaluation approaches are statistics-based, identifying the optimal transformation by maximizing the number of consistent correspondences. However, registration recall decreases significantly when point clouds exhibit a low overlap rate, despite efforts in designing feature descriptors and establishing correspondences. In this paper, we introduce Deep-PE, a lightweight, learning-based pose evaluator designed to enhance the accuracy of pose selection, especially in challenging point cloud scenarios with low overlap. Our network i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.16085","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/2405.16085/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":"2405.16085","created_at":"2026-07-05T08:23:25.602039+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.16085v1","created_at":"2026-07-05T08:23:25.602039+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.16085","created_at":"2026-07-05T08:23:25.602039+00:00"},{"alias_kind":"pith_short_12","alias_value":"IBUXFM5GRON6","created_at":"2026-07-05T08:23:25.602039+00:00"},{"alias_kind":"pith_short_16","alias_value":"IBUXFM5GRON6E3IB","created_at":"2026-07-05T08:23:25.602039+00:00"},{"alias_kind":"pith_short_8","alias_value":"IBUXFM5G","created_at":"2026-07-05T08:23:25.602039+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/IBUXFM5GRON6E3IBZXXHCEQLKA","json":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA.json","graph_json":"https://pith.science/api/pith-number/IBUXFM5GRON6E3IBZXXHCEQLKA/graph.json","events_json":"https://pith.science/api/pith-number/IBUXFM5GRON6E3IBZXXHCEQLKA/events.json","paper":"https://pith.science/paper/IBUXFM5G"},"agent_actions":{"view_html":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA","download_json":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA.json","view_paper":"https://pith.science/paper/IBUXFM5G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.16085&json=true","fetch_graph":"https://pith.science/api/pith-number/IBUXFM5GRON6E3IBZXXHCEQLKA/graph.json","fetch_events":"https://pith.science/api/pith-number/IBUXFM5GRON6E3IBZXXHCEQLKA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA/action/storage_attestation","attest_author":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA/action/author_attestation","sign_citation":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA/action/citation_signature","submit_replication":"https://pith.science/pith/IBUXFM5GRON6E3IBZXXHCEQLKA/action/replication_record"}},"created_at":"2026-07-05T08:23:25.602039+00:00","updated_at":"2026-07-05T08:23:25.602039+00:00"}