{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Q4PFTMURAWGOEYZBMP3S435JBG","short_pith_number":"pith:Q4PFTMUR","schema_version":"1.0","canonical_sha256":"871e59b291058ce2632163f72e6fa909b28ae87d776c49cb90f4c149c75f6b93","source":{"kind":"arxiv","id":"2411.01408","version":1},"attestation_state":"computed","paper":{"title":"HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hao Zhang, Jianru Xue, Jianwu Fang, Shanmin Pang, Wenzhao Qiu","submitted_at":"2024-11-03T02:35:17Z","abstract_excerpt":"Recent advances in high-definition (HD) map construction from surround-view images have highlighted their cost-effectiveness in deployment. However, prevailing techniques often fall short in accurately extracting and utilizing road features, as well as in the implementation of view transformation. In response, we introduce HeightMapNet, a novel framework that establishes a dynamic relationship between image features and road surface height distributions. By integrating height priors, our approach refines the accuracy of Bird's-Eye-View (BEV) features beyond conventional methods. HeightMapNet a"},"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":"2411.01408","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-03T02:35:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"efb488fc49d93edb45cb555bd49eae7598519b5ce8990f1af8ee7912e924b12b","abstract_canon_sha256":"c955c09de7ef3e16dd300541b95e37ff612dd0fb0c862d95881570012c106ef4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:30:29.960232Z","signature_b64":"RpTjreyJJ+Hsr0/TWV07AoAEIJ/h+8eZn8NkSGt/G/h/ZYyNoUFe8UlD3bO26eBbt/HVdASccjP7CMxkFWU0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"871e59b291058ce2632163f72e6fa909b28ae87d776c49cb90f4c149c75f6b93","last_reissued_at":"2026-07-05T09:30:29.959756Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:30:29.959756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HeightMapNet: Explicit Height Modeling for End-to-End HD Map Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hao Zhang, Jianru Xue, Jianwu Fang, Shanmin Pang, Wenzhao Qiu","submitted_at":"2024-11-03T02:35:17Z","abstract_excerpt":"Recent advances in high-definition (HD) map construction from surround-view images have highlighted their cost-effectiveness in deployment. However, prevailing techniques often fall short in accurately extracting and utilizing road features, as well as in the implementation of view transformation. In response, we introduce HeightMapNet, a novel framework that establishes a dynamic relationship between image features and road surface height distributions. By integrating height priors, our approach refines the accuracy of Bird's-Eye-View (BEV) features beyond conventional methods. HeightMapNet a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.01408","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/2411.01408/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":"2411.01408","created_at":"2026-07-05T09:30:29.959813+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.01408v1","created_at":"2026-07-05T09:30:29.959813+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.01408","created_at":"2026-07-05T09:30:29.959813+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q4PFTMURAWGO","created_at":"2026-07-05T09:30:29.959813+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q4PFTMURAWGOEYZB","created_at":"2026-07-05T09:30:29.959813+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q4PFTMUR","created_at":"2026-07-05T09:30:29.959813+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.10409","citing_title":"Dataset Construction for Training LLM to Learn Analog Circuit Knowledge","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG","json":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG.json","graph_json":"https://pith.science/api/pith-number/Q4PFTMURAWGOEYZBMP3S435JBG/graph.json","events_json":"https://pith.science/api/pith-number/Q4PFTMURAWGOEYZBMP3S435JBG/events.json","paper":"https://pith.science/paper/Q4PFTMUR"},"agent_actions":{"view_html":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG","download_json":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG.json","view_paper":"https://pith.science/paper/Q4PFTMUR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.01408&json=true","fetch_graph":"https://pith.science/api/pith-number/Q4PFTMURAWGOEYZBMP3S435JBG/graph.json","fetch_events":"https://pith.science/api/pith-number/Q4PFTMURAWGOEYZBMP3S435JBG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG/action/storage_attestation","attest_author":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG/action/author_attestation","sign_citation":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG/action/citation_signature","submit_replication":"https://pith.science/pith/Q4PFTMURAWGOEYZBMP3S435JBG/action/replication_record"}},"created_at":"2026-07-05T09:30:29.959813+00:00","updated_at":"2026-07-05T09:30:29.959813+00:00"}