{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3IN3FRVH6YGVB3QB6QPRHKV43I","short_pith_number":"pith:3IN3FRVH","schema_version":"1.0","canonical_sha256":"da1bb2c6a7f60d50ee01f41f13aabcda0e85c5adabf1d85730c1d50a00bdaa9e","source":{"kind":"arxiv","id":"2403.10821","version":3},"attestation_state":"computed","paper":{"title":"H3-Mapping: Quasi-Heterogeneous Feature Grids for Real-time Dense Mapping Using Hierarchical Hybrid Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Boyu Zhou, Chenxing Jiang, Shaojie Shen, Yiming Luo","submitted_at":"2024-03-16T06:14:04Z","abstract_excerpt":"In recent years, implicit online dense mapping methods have achieved high-quality reconstruction results, showcasing great potential in robotics, AR/VR, and digital twins applications. However, existing methods struggle with slow texture modeling which limits their real-time performance. To address these limitations, we propose a NeRF-based dense mapping method that enables faster and higher-quality reconstruction. To improve texture modeling, we introduce quasi-heterogeneous feature grids, which inherit the fast querying ability of uniform feature grids while adapting to varying levels of tex"},"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":"2403.10821","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-03-16T06:14:04Z","cross_cats_sorted":[],"title_canon_sha256":"b876935b16b1cd7eb095344a7ccc42afbbaa692c4c973765799b4f78685b0a83","abstract_canon_sha256":"bf3a2c1f5b008a60e9182fe912847051ae72e95c1eec92cbd4e1497524cc7769"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:12.814980Z","signature_b64":"6A/JOzErMle86cGUzB1uZaen77URKFyeINVw9rG1ticMkvVarnvenxLcar+cI6tgmPLoWwzDcSy70eUj2umhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da1bb2c6a7f60d50ee01f41f13aabcda0e85c5adabf1d85730c1d50a00bdaa9e","last_reissued_at":"2026-07-05T08:44:12.814490Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:12.814490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"H3-Mapping: Quasi-Heterogeneous Feature Grids for Real-time Dense Mapping Using Hierarchical Hybrid Representation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Boyu Zhou, Chenxing Jiang, Shaojie Shen, Yiming Luo","submitted_at":"2024-03-16T06:14:04Z","abstract_excerpt":"In recent years, implicit online dense mapping methods have achieved high-quality reconstruction results, showcasing great potential in robotics, AR/VR, and digital twins applications. However, existing methods struggle with slow texture modeling which limits their real-time performance. To address these limitations, we propose a NeRF-based dense mapping method that enables faster and higher-quality reconstruction. To improve texture modeling, we introduce quasi-heterogeneous feature grids, which inherit the fast querying ability of uniform feature grids while adapting to varying levels of tex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.10821","kind":"arxiv","version":3},"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/2403.10821/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":"2403.10821","created_at":"2026-07-05T08:44:12.814549+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.10821v3","created_at":"2026-07-05T08:44:12.814549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.10821","created_at":"2026-07-05T08:44:12.814549+00:00"},{"alias_kind":"pith_short_12","alias_value":"3IN3FRVH6YGV","created_at":"2026-07-05T08:44:12.814549+00:00"},{"alias_kind":"pith_short_16","alias_value":"3IN3FRVH6YGVB3QB","created_at":"2026-07-05T08:44:12.814549+00:00"},{"alias_kind":"pith_short_8","alias_value":"3IN3FRVH","created_at":"2026-07-05T08:44:12.814549+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.08672","citing_title":"GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multi-sensor Fused Odometry with Gaussian Mapping","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I","json":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I.json","graph_json":"https://pith.science/api/pith-number/3IN3FRVH6YGVB3QB6QPRHKV43I/graph.json","events_json":"https://pith.science/api/pith-number/3IN3FRVH6YGVB3QB6QPRHKV43I/events.json","paper":"https://pith.science/paper/3IN3FRVH"},"agent_actions":{"view_html":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I","download_json":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I.json","view_paper":"https://pith.science/paper/3IN3FRVH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.10821&json=true","fetch_graph":"https://pith.science/api/pith-number/3IN3FRVH6YGVB3QB6QPRHKV43I/graph.json","fetch_events":"https://pith.science/api/pith-number/3IN3FRVH6YGVB3QB6QPRHKV43I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I/action/storage_attestation","attest_author":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I/action/author_attestation","sign_citation":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I/action/citation_signature","submit_replication":"https://pith.science/pith/3IN3FRVH6YGVB3QB6QPRHKV43I/action/replication_record"}},"created_at":"2026-07-05T08:44:12.814549+00:00","updated_at":"2026-07-05T08:44:12.814549+00:00"}