{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VPBIKOFOEBCLA4PDSV6TQ5I32C","short_pith_number":"pith:VPBIKOFO","schema_version":"1.0","canonical_sha256":"abc28538ae2044b071e3957d38751bd0abb4a541246d9fa24f13959976ecdedd","source":{"kind":"arxiv","id":"2505.09422","version":1},"attestation_state":"computed","paper":{"title":"MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bierzynski Kay, Lorenzo Servadei, Miao Tang, Robert Wille, Xiangyuan Peng, Yu Wang","submitted_at":"2025-05-14T14:23:33Z","abstract_excerpt":"Reliable autonomous driving systems require accurate detection of traffic participants. To this end, multi-modal fusion has emerged as an effective strategy. In particular, 4D radar and LiDAR fusion methods based on multi-frame radar point clouds have demonstrated the effectiveness in bridging the point density gap. However, they often neglect radar point clouds' inter-frame misalignment caused by object movement during accumulation and do not fully exploit the object dynamic information from 4D radar. In this paper, we propose MoRAL, a motion-aware multi-frame 4D radar and LiDAR fusion framew"},"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":"2505.09422","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-14T14:23:33Z","cross_cats_sorted":[],"title_canon_sha256":"29e3bad3ffee2b47cad7803ca861962abc9682dfb376da18686480ab24fe202c","abstract_canon_sha256":"57371de40175c9cce2ddc3dac1fcd9cf9c23e0c2dc99acd358bbb805830444fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:08.371279Z","signature_b64":"QhFKuR/X8E9pm+79Rhcpfag4jH01ANIX9LOIeVGEzewH6DUqWMdWoux7TTurYKbUhKsVpwfBpP5OSi3UEiKGBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"abc28538ae2044b071e3957d38751bd0abb4a541246d9fa24f13959976ecdedd","last_reissued_at":"2026-07-05T11:03:08.370792Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:08.370792Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bierzynski Kay, Lorenzo Servadei, Miao Tang, Robert Wille, Xiangyuan Peng, Yu Wang","submitted_at":"2025-05-14T14:23:33Z","abstract_excerpt":"Reliable autonomous driving systems require accurate detection of traffic participants. To this end, multi-modal fusion has emerged as an effective strategy. In particular, 4D radar and LiDAR fusion methods based on multi-frame radar point clouds have demonstrated the effectiveness in bridging the point density gap. However, they often neglect radar point clouds' inter-frame misalignment caused by object movement during accumulation and do not fully exploit the object dynamic information from 4D radar. In this paper, we propose MoRAL, a motion-aware multi-frame 4D radar and LiDAR fusion framew"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09422","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/2505.09422/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":"2505.09422","created_at":"2026-07-05T11:03:08.370850+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.09422v1","created_at":"2026-07-05T11:03:08.370850+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09422","created_at":"2026-07-05T11:03:08.370850+00:00"},{"alias_kind":"pith_short_12","alias_value":"VPBIKOFOEBCL","created_at":"2026-07-05T11:03:08.370850+00:00"},{"alias_kind":"pith_short_16","alias_value":"VPBIKOFOEBCLA4PD","created_at":"2026-07-05T11:03:08.370850+00:00"},{"alias_kind":"pith_short_8","alias_value":"VPBIKOFO","created_at":"2026-07-05T11:03:08.370850+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01983","citing_title":"Open-Weather Robust 3D Detection via Dual-Critic Diffusion Alignment","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2510.26641","citing_title":"All You Need for Object Detection: From Pixels, Points, and Prompts to Next-Gen Fusion and Multimodal LLMs/VLMs in Autonomous Vehicles","ref_index":257,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C","json":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C.json","graph_json":"https://pith.science/api/pith-number/VPBIKOFOEBCLA4PDSV6TQ5I32C/graph.json","events_json":"https://pith.science/api/pith-number/VPBIKOFOEBCLA4PDSV6TQ5I32C/events.json","paper":"https://pith.science/paper/VPBIKOFO"},"agent_actions":{"view_html":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C","download_json":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C.json","view_paper":"https://pith.science/paper/VPBIKOFO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.09422&json=true","fetch_graph":"https://pith.science/api/pith-number/VPBIKOFOEBCLA4PDSV6TQ5I32C/graph.json","fetch_events":"https://pith.science/api/pith-number/VPBIKOFOEBCLA4PDSV6TQ5I32C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C/action/storage_attestation","attest_author":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C/action/author_attestation","sign_citation":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C/action/citation_signature","submit_replication":"https://pith.science/pith/VPBIKOFOEBCLA4PDSV6TQ5I32C/action/replication_record"}},"created_at":"2026-07-05T11:03:08.370850+00:00","updated_at":"2026-07-05T11:03:08.370850+00:00"}