{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YN2EQQXW2B7GSR6U5E6MIT66UG","short_pith_number":"pith:YN2EQQXW","schema_version":"1.0","canonical_sha256":"c3744842f6d07e6947d4e93cc44fdea1b35838950e269c6ed56c7c23470ef83f","source":{"kind":"arxiv","id":"2405.06782","version":1},"attestation_state":"computed","paper":{"title":"GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alois Knoll, Bare Luka Zagar, Ekim Yurtsever, Jun Meng, Marc Brede, Mingyu Liu, Walter Zimmer, Xingcheng Zhou, Yuning Cui","submitted_at":"2024-05-10T19:18:02Z","abstract_excerpt":"Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibited promising performance. However, these methods refine proposals individually, ignoring the rich contextual information in the object relationships between the neighbor proposals. In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection. Specifically, we create an inte"},"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":"2405.06782","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-10T19:18:02Z","cross_cats_sorted":[],"title_canon_sha256":"f3d431300b3ddd2e2a8b2afc5bff8947f3cdf1da86cf5e330a99954e821d9b35","abstract_canon_sha256":"897c9c6ad5fad4b1d8aa3050b61230ba6bb4bcc2ce1c53e6f4c150eaffab23cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:18:11.817599Z","signature_b64":"1gyz2Xrj2IX4x0+5s8amXuiBfOWzXmU5OW3+YYt452LyGneqH1Un3jxcMWZ6f8kXlN3nkmlR724HBoTWaKB8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3744842f6d07e6947d4e93cc44fdea1b35838950e269c6ed56c7c23470ef83f","last_reissued_at":"2026-07-05T08:18:11.817188Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:18:11.817188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alois Knoll, Bare Luka Zagar, Ekim Yurtsever, Jun Meng, Marc Brede, Mingyu Liu, Walter Zimmer, Xingcheng Zhou, Yuning Cui","submitted_at":"2024-05-10T19:18:02Z","abstract_excerpt":"Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibited promising performance. However, these methods refine proposals individually, ignoring the rich contextual information in the object relationships between the neighbor proposals. In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection. Specifically, we create an inte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06782","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/2405.06782/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":"2405.06782","created_at":"2026-07-05T08:18:11.817244+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.06782v1","created_at":"2026-07-05T08:18:11.817244+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06782","created_at":"2026-07-05T08:18:11.817244+00:00"},{"alias_kind":"pith_short_12","alias_value":"YN2EQQXW2B7G","created_at":"2026-07-05T08:18:11.817244+00:00"},{"alias_kind":"pith_short_16","alias_value":"YN2EQQXW2B7GSR6U","created_at":"2026-07-05T08:18:11.817244+00:00"},{"alias_kind":"pith_short_8","alias_value":"YN2EQQXW","created_at":"2026-07-05T08:18:11.817244+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/YN2EQQXW2B7GSR6U5E6MIT66UG","json":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG.json","graph_json":"https://pith.science/api/pith-number/YN2EQQXW2B7GSR6U5E6MIT66UG/graph.json","events_json":"https://pith.science/api/pith-number/YN2EQQXW2B7GSR6U5E6MIT66UG/events.json","paper":"https://pith.science/paper/YN2EQQXW"},"agent_actions":{"view_html":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG","download_json":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG.json","view_paper":"https://pith.science/paper/YN2EQQXW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.06782&json=true","fetch_graph":"https://pith.science/api/pith-number/YN2EQQXW2B7GSR6U5E6MIT66UG/graph.json","fetch_events":"https://pith.science/api/pith-number/YN2EQQXW2B7GSR6U5E6MIT66UG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG/action/storage_attestation","attest_author":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG/action/author_attestation","sign_citation":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG/action/citation_signature","submit_replication":"https://pith.science/pith/YN2EQQXW2B7GSR6U5E6MIT66UG/action/replication_record"}},"created_at":"2026-07-05T08:18:11.817244+00:00","updated_at":"2026-07-05T08:18:11.817244+00:00"}