{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:C2AMDREE2GA6JL4AWSZK657DPG","short_pith_number":"pith:C2AMDREE","schema_version":"1.0","canonical_sha256":"1680c1c484d181e4af80b4b2af77e379a50e987b6a19087eee0d0ff45528a16d","source":{"kind":"arxiv","id":"2504.18325","version":2},"attestation_state":"computed","paper":{"title":"Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Tan, Dongxin Lyu, Han Huang, Zimu Li","submitted_at":"2025-04-25T13:08:41Z","abstract_excerpt":"Monocular 3D lane detection is challenging due to the difficulty in capturing depth information from single-camera images. A common strategy involves transforming front-view (FV) images into bird's-eye-view (BEV) space through inverse perspective mapping (IPM), facilitating lane detection using BEV features. However, IPM's flat-ground assumption and loss of contextual information lead to inaccuracies in reconstructing 3D information, especially height. In this paper, we introduce a BEV-based framework to address these limitations and improve 3D lane detection accuracy. Our approach incorporate"},"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":"2504.18325","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-25T13:08:41Z","cross_cats_sorted":[],"title_canon_sha256":"06dcdee3c0b5d6191efe574ef5d3117e05688e4e8053497894e4d65ea6c6047e","abstract_canon_sha256":"7860f637d6828edaa6c1d28627431ed565ce969ac0e1b1cb058e8256332d14f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:45.310373Z","signature_b64":"4wHPP9sj3FU9MF5lb83G7k3qG13izFI28b6TG3nrYHBvPAKmA1KmwlJ7hDLKj7whSxjSt//ZPycZwObcNli9AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1680c1c484d181e4af80b4b2af77e379a50e987b6a19087eee0d0ff45528a16d","last_reissued_at":"2026-07-05T12:02:45.309835Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:45.309835Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cheng Tan, Dongxin Lyu, Han Huang, Zimu Li","submitted_at":"2025-04-25T13:08:41Z","abstract_excerpt":"Monocular 3D lane detection is challenging due to the difficulty in capturing depth information from single-camera images. A common strategy involves transforming front-view (FV) images into bird's-eye-view (BEV) space through inverse perspective mapping (IPM), facilitating lane detection using BEV features. However, IPM's flat-ground assumption and loss of contextual information lead to inaccuracies in reconstructing 3D information, especially height. In this paper, we introduce a BEV-based framework to address these limitations and improve 3D lane detection accuracy. Our approach incorporate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18325","kind":"arxiv","version":2},"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/2504.18325/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":"2504.18325","created_at":"2026-07-05T12:02:45.309900+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.18325v2","created_at":"2026-07-05T12:02:45.309900+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18325","created_at":"2026-07-05T12:02:45.309900+00:00"},{"alias_kind":"pith_short_12","alias_value":"C2AMDREE2GA6","created_at":"2026-07-05T12:02:45.309900+00:00"},{"alias_kind":"pith_short_16","alias_value":"C2AMDREE2GA6JL4A","created_at":"2026-07-05T12:02:45.309900+00:00"},{"alias_kind":"pith_short_8","alias_value":"C2AMDREE","created_at":"2026-07-05T12:02:45.309900+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31172","citing_title":"HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG","json":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG.json","graph_json":"https://pith.science/api/pith-number/C2AMDREE2GA6JL4AWSZK657DPG/graph.json","events_json":"https://pith.science/api/pith-number/C2AMDREE2GA6JL4AWSZK657DPG/events.json","paper":"https://pith.science/paper/C2AMDREE"},"agent_actions":{"view_html":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG","download_json":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG.json","view_paper":"https://pith.science/paper/C2AMDREE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.18325&json=true","fetch_graph":"https://pith.science/api/pith-number/C2AMDREE2GA6JL4AWSZK657DPG/graph.json","fetch_events":"https://pith.science/api/pith-number/C2AMDREE2GA6JL4AWSZK657DPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG/action/storage_attestation","attest_author":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG/action/author_attestation","sign_citation":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG/action/citation_signature","submit_replication":"https://pith.science/pith/C2AMDREE2GA6JL4AWSZK657DPG/action/replication_record"}},"created_at":"2026-07-05T12:02:45.309900+00:00","updated_at":"2026-07-05T12:02:45.309900+00:00"}