{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BSEIFS6GBEP3TCC6PFCY6MGHIP","short_pith_number":"pith:BSEIFS6G","schema_version":"1.0","canonical_sha256":"0c8882cbc6091fb9885e79458f30c743f10d797ed6a4ffe8904e51b6f457436e","source":{"kind":"arxiv","id":"2211.07214","version":3},"attestation_state":"computed","paper":{"title":"Robust Collaborative 3D Object Detection in Presence of Pose Errors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA","cs.RO"],"primary_cat":"cs.CV","authors_text":"Baoan Liu, Chen Feng, Mehrdad Dianati, Quanhao Li, Siheng Chen, Yanfeng Wang, Yifan Lu","submitted_at":"2022-11-14T09:11:14Z","abstract_excerpt":"Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. However, in practice, pose estimation errors due to imperfect localization would cause spatial message misalignment and significantly reduce the performance of collaboration. To alleviate adverse impacts of pose errors, we propose CoAlign, a novel hybrid collaboration framework that is robust to unknown pose errors. The proposed solution relies on a novel agent-object pose graph modeling to enhance pose consistency am"},"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":"2211.07214","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-14T09:11:14Z","cross_cats_sorted":["cs.MA","cs.RO"],"title_canon_sha256":"221a476d2801de2193f62590e3a307231f4d0153adf8dea652381b54e23ead0a","abstract_canon_sha256":"bf335b38e425afed542f224502a0c49c9baa8e98de0b3d68782829949431fa4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:33.820746Z","signature_b64":"UxeI+wK4MA4a84uw8yDkUj2fc7EcOo8XTrfHqmwkymWWtNFEXV5O38n0xHTqUnhX7uDLyIbiGRT1mqKlATY4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c8882cbc6091fb9885e79458f30c743f10d797ed6a4ffe8904e51b6f457436e","last_reissued_at":"2026-07-05T05:47:33.820242Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:33.820242Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Collaborative 3D Object Detection in Presence of Pose Errors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA","cs.RO"],"primary_cat":"cs.CV","authors_text":"Baoan Liu, Chen Feng, Mehrdad Dianati, Quanhao Li, Siheng Chen, Yanfeng Wang, Yifan Lu","submitted_at":"2022-11-14T09:11:14Z","abstract_excerpt":"Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. However, in practice, pose estimation errors due to imperfect localization would cause spatial message misalignment and significantly reduce the performance of collaboration. To alleviate adverse impacts of pose errors, we propose CoAlign, a novel hybrid collaboration framework that is robust to unknown pose errors. The proposed solution relies on a novel agent-object pose graph modeling to enhance pose consistency am"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07214","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/2211.07214/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":"2211.07214","created_at":"2026-07-05T05:47:33.820294+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.07214v3","created_at":"2026-07-05T05:47:33.820294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07214","created_at":"2026-07-05T05:47:33.820294+00:00"},{"alias_kind":"pith_short_12","alias_value":"BSEIFS6GBEP3","created_at":"2026-07-05T05:47:33.820294+00:00"},{"alias_kind":"pith_short_16","alias_value":"BSEIFS6GBEP3TCC6","created_at":"2026-07-05T05:47:33.820294+00:00"},{"alias_kind":"pith_short_8","alias_value":"BSEIFS6G","created_at":"2026-07-05T05:47:33.820294+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01827","citing_title":"C2E: Boosting Ego-Only 3D Object Detection via Multi-Teacher Contrastive Knowledge Distillation","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22122","citing_title":"Adversarial Trust Poisoning in Vehicular Collaborative Perception","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07910","citing_title":"One World, Dual Timeline: Decoupled Spatio-Temporal Gaussian Scene Graph for 4D Cooperative Driving Reconstruction","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07910","citing_title":"One World, Dual Timeline: Decoupled Spatio-Temporal Gaussian Scene Graph for 4D Cooperative Driving Reconstruction","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP","json":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP.json","graph_json":"https://pith.science/api/pith-number/BSEIFS6GBEP3TCC6PFCY6MGHIP/graph.json","events_json":"https://pith.science/api/pith-number/BSEIFS6GBEP3TCC6PFCY6MGHIP/events.json","paper":"https://pith.science/paper/BSEIFS6G"},"agent_actions":{"view_html":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP","download_json":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP.json","view_paper":"https://pith.science/paper/BSEIFS6G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.07214&json=true","fetch_graph":"https://pith.science/api/pith-number/BSEIFS6GBEP3TCC6PFCY6MGHIP/graph.json","fetch_events":"https://pith.science/api/pith-number/BSEIFS6GBEP3TCC6PFCY6MGHIP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP/action/storage_attestation","attest_author":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP/action/author_attestation","sign_citation":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP/action/citation_signature","submit_replication":"https://pith.science/pith/BSEIFS6GBEP3TCC6PFCY6MGHIP/action/replication_record"}},"created_at":"2026-07-05T05:47:33.820294+00:00","updated_at":"2026-07-05T05:47:33.820294+00:00"}