{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FGWE3UK2CAJQQK5MDWSAPT6JHY","short_pith_number":"pith:FGWE3UK2","schema_version":"1.0","canonical_sha256":"29ac4dd15a1013082bac1da407cfc93e0f1d13d4735250c6e1751c072d577eeb","source":{"kind":"arxiv","id":"2605.21112","version":1},"attestation_state":"computed","paper":{"title":"RCGDet3D: Rethinking 4D Radar-Camera Fusion-based 3D Object Detection with Enhanced Radar Feature Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Zhu, Weiyi Xiong","submitted_at":"2026-05-20T12:45:01Z","abstract_excerpt":"4D automotive radar is indispensable for autonomous driving due to its low cost and robustness, yet its point cloud sparsity challenges 3D object detection. Existing 4D radar-camera fusion methods focus on complex fusion strategies, trading inference speed for marginal gains. This trade-off hinders real-time deployment due to heavy computation on dense feature maps. In contrast, feature extraction from sparse radar points is less time-consuming but remains under-explored. This work uncovers that simply enhancing radar feature extraction can achieve comparable or even higher performance than el"},"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":"2605.21112","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-20T12:45:01Z","cross_cats_sorted":[],"title_canon_sha256":"b50b9e412bb0fba1291130a5f94cd52e2aed5a01087b705e9ced046f3541f5fa","abstract_canon_sha256":"575b30c43d7b1e3e9b1d0cf8d337d0194d49855854744df07b156288647425ac"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:38.067330Z","signature_b64":"L+FE+NkUs4RzJhYXpTDrUAS+TvVxGzYfkj/4mZkILpQDx3PK2lmroQyD/JjnM2CtXk6eWS4GroFJ8g+rv7DkAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29ac4dd15a1013082bac1da407cfc93e0f1d13d4735250c6e1751c072d577eeb","last_reissued_at":"2026-05-21T01:05:38.066571Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:38.066571Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RCGDet3D: Rethinking 4D Radar-Camera Fusion-based 3D Object Detection with Enhanced Radar Feature Encoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Zhu, Weiyi Xiong","submitted_at":"2026-05-20T12:45:01Z","abstract_excerpt":"4D automotive radar is indispensable for autonomous driving due to its low cost and robustness, yet its point cloud sparsity challenges 3D object detection. Existing 4D radar-camera fusion methods focus on complex fusion strategies, trading inference speed for marginal gains. This trade-off hinders real-time deployment due to heavy computation on dense feature maps. In contrast, feature extraction from sparse radar points is less time-consuming but remains under-explored. This work uncovers that simply enhancing radar feature extraction can achieve comparable or even higher performance than el"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21112","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/2605.21112/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":"2605.21112","created_at":"2026-05-21T01:05:38.066686+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.21112v1","created_at":"2026-05-21T01:05:38.066686+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21112","created_at":"2026-05-21T01:05:38.066686+00:00"},{"alias_kind":"pith_short_12","alias_value":"FGWE3UK2CAJQ","created_at":"2026-05-21T01:05:38.066686+00:00"},{"alias_kind":"pith_short_16","alias_value":"FGWE3UK2CAJQQK5M","created_at":"2026-05-21T01:05:38.066686+00:00"},{"alias_kind":"pith_short_8","alias_value":"FGWE3UK2","created_at":"2026-05-21T01:05:38.066686+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/FGWE3UK2CAJQQK5MDWSAPT6JHY","json":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY.json","graph_json":"https://pith.science/api/pith-number/FGWE3UK2CAJQQK5MDWSAPT6JHY/graph.json","events_json":"https://pith.science/api/pith-number/FGWE3UK2CAJQQK5MDWSAPT6JHY/events.json","paper":"https://pith.science/paper/FGWE3UK2"},"agent_actions":{"view_html":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY","download_json":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY.json","view_paper":"https://pith.science/paper/FGWE3UK2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.21112&json=true","fetch_graph":"https://pith.science/api/pith-number/FGWE3UK2CAJQQK5MDWSAPT6JHY/graph.json","fetch_events":"https://pith.science/api/pith-number/FGWE3UK2CAJQQK5MDWSAPT6JHY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY/action/storage_attestation","attest_author":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY/action/author_attestation","sign_citation":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY/action/citation_signature","submit_replication":"https://pith.science/pith/FGWE3UK2CAJQQK5MDWSAPT6JHY/action/replication_record"}},"created_at":"2026-05-21T01:05:38.066686+00:00","updated_at":"2026-05-21T01:05:38.066686+00:00"}