{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MUCG43MCG4DQEJFKHOIPV5N3DX","short_pith_number":"pith:MUCG43MC","schema_version":"1.0","canonical_sha256":"65046e6d8237070224aa3b90faf5bb1de0d242bea58f52e0d54cd73fef5342a0","source":{"kind":"arxiv","id":"2311.11722","version":1},"attestation_state":"computed","paper":{"title":"Sparse4D v3: Advancing End-to-End 3D Detection and Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Lichao Huang, Tianwei Lin, Xuewu Lin, Zhizhong Su, Zixiang Pei","submitted_at":"2023-11-20T12:37:58Z","abstract_excerpt":"In autonomous driving perception systems, 3D detection and tracking are the two fundamental tasks. This paper delves deeper into this field, building upon the Sparse4D framework. We introduce two auxiliary training tasks (Temporal Instance Denoising and Quality Estimation) and propose decoupled attention to make structural improvements, leading to significant enhancements in detection performance. Additionally, we extend the detector into a tracker using a straightforward approach that assigns instance ID during inference, further highlighting the advantages of query-based algorithms. Extensiv"},"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":"2311.11722","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-11-20T12:37:58Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"e044e5d619913a872e7639d2c8588117edd0104e1381ad2f9dc25080a169a000","abstract_canon_sha256":"119ed0ab698828b0e31941d4abf1a006c4fa3e5aa7c75ba77a6702108640402e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:14:38.188756Z","signature_b64":"spXceNSe/O6Bp9SLNFVwXxij1MPuO3TlbOq69S3seJ69Q8HMH3l62+8GXAaAJQGCaYwUWLJiyWmAEPN+duDmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65046e6d8237070224aa3b90faf5bb1de0d242bea58f52e0d54cd73fef5342a0","last_reissued_at":"2026-07-05T07:14:38.188358Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:14:38.188358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse4D v3: Advancing End-to-End 3D Detection and Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Lichao Huang, Tianwei Lin, Xuewu Lin, Zhizhong Su, Zixiang Pei","submitted_at":"2023-11-20T12:37:58Z","abstract_excerpt":"In autonomous driving perception systems, 3D detection and tracking are the two fundamental tasks. This paper delves deeper into this field, building upon the Sparse4D framework. We introduce two auxiliary training tasks (Temporal Instance Denoising and Quality Estimation) and propose decoupled attention to make structural improvements, leading to significant enhancements in detection performance. Additionally, we extend the detector into a tracker using a straightforward approach that assigns instance ID during inference, further highlighting the advantages of query-based algorithms. Extensiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.11722","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/2311.11722/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":"2311.11722","created_at":"2026-07-05T07:14:38.188418+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.11722v1","created_at":"2026-07-05T07:14:38.188418+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.11722","created_at":"2026-07-05T07:14:38.188418+00:00"},{"alias_kind":"pith_short_12","alias_value":"MUCG43MCG4DQ","created_at":"2026-07-05T07:14:38.188418+00:00"},{"alias_kind":"pith_short_16","alias_value":"MUCG43MCG4DQEJFK","created_at":"2026-07-05T07:14:38.188418+00:00"},{"alias_kind":"pith_short_8","alias_value":"MUCG43MC","created_at":"2026-07-05T07:14:38.188418+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25736","citing_title":"UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2506.11419","citing_title":"FocalAD: Local Motion Planning for End-to-End Autonomous Driving","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.00813","citing_title":"DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25405","citing_title":"Leveraging Previous-Traversal Point Cloud Map Priors for Camera-Based 3D Object Detection and Tracking","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25574","citing_title":"Control Your Queries: Heterogeneous Query Interaction for Camera-Radar Fusion","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01924","citing_title":"SimPB++: Simultaneously Detecting 2D and 3D Objects from Multiple Cameras","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17024","citing_title":"CAM3DNet: Comprehensively mining the multi-scale features for 3D Object Detection with Multi-View Cameras","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX","json":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX.json","graph_json":"https://pith.science/api/pith-number/MUCG43MCG4DQEJFKHOIPV5N3DX/graph.json","events_json":"https://pith.science/api/pith-number/MUCG43MCG4DQEJFKHOIPV5N3DX/events.json","paper":"https://pith.science/paper/MUCG43MC"},"agent_actions":{"view_html":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX","download_json":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX.json","view_paper":"https://pith.science/paper/MUCG43MC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.11722&json=true","fetch_graph":"https://pith.science/api/pith-number/MUCG43MCG4DQEJFKHOIPV5N3DX/graph.json","fetch_events":"https://pith.science/api/pith-number/MUCG43MCG4DQEJFKHOIPV5N3DX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX/action/storage_attestation","attest_author":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX/action/author_attestation","sign_citation":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX/action/citation_signature","submit_replication":"https://pith.science/pith/MUCG43MCG4DQEJFKHOIPV5N3DX/action/replication_record"}},"created_at":"2026-07-05T07:14:38.188418+00:00","updated_at":"2026-07-05T07:14:38.188418+00:00"}