{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:FVJX3BQ6PCSDAKXAS57AT6J5RR","short_pith_number":"pith:FVJX3BQ6","schema_version":"1.0","canonical_sha256":"2d537d861e78a4302ae0977e09f93d8c46931295017704903161f0958e15d0c2","source":{"kind":"arxiv","id":"2503.10170","version":2},"attestation_state":"computed","paper":{"title":"GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Bowen Wang, Chunran Zheng, Fu Zhang, Jianheng Liu, Jiarong Lin, Yunfei Wan","submitted_at":"2025-03-13T08:53:38Z","abstract_excerpt":"Digital twins are fundamental to the development of autonomous driving and embodied artificial intelligence. However, achieving high-granularity surface reconstruction and high-fidelity rendering remains a challenge. Gaussian splatting offers efficient photorealistic rendering but struggles with geometric inconsistencies due to fragmented primitives and sparse observational data in robotics applications. Existing regularization methods, which rely on render-derived constraints, often fail in complex environments. Moreover, effectively integrating sparse LiDAR data with Gaussian splatting remai"},"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":"2503.10170","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-03-13T08:53:38Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"beaa3def32f4f8bd6df2a549c637f0d5c0243a80dc39c3f8d8097e4db683237c","abstract_canon_sha256":"5fd464378cb5073f2fc0c5734da9b39b9e649bdbd51e389be517e29fd095463d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:44:46.518786Z","signature_b64":"y7oUDGFchT4W/fa42TVTDOTGeNXifxskvKnyB7GQqP5W2qTRJchdgj/hAqA67oVsZbqhi08/z+mcpd13xfnmBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d537d861e78a4302ae0977e09f93d8c46931295017704903161f0958e15d0c2","last_reissued_at":"2026-07-05T11:44:46.518303Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:44:46.518303Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Bowen Wang, Chunran Zheng, Fu Zhang, Jianheng Liu, Jiarong Lin, Yunfei Wan","submitted_at":"2025-03-13T08:53:38Z","abstract_excerpt":"Digital twins are fundamental to the development of autonomous driving and embodied artificial intelligence. However, achieving high-granularity surface reconstruction and high-fidelity rendering remains a challenge. Gaussian splatting offers efficient photorealistic rendering but struggles with geometric inconsistencies due to fragmented primitives and sparse observational data in robotics applications. Existing regularization methods, which rely on render-derived constraints, often fail in complex environments. Moreover, effectively integrating sparse LiDAR data with Gaussian splatting remai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.10170","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/2503.10170/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":"2503.10170","created_at":"2026-07-05T11:44:46.518369+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.10170v2","created_at":"2026-07-05T11:44:46.518369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.10170","created_at":"2026-07-05T11:44:46.518369+00:00"},{"alias_kind":"pith_short_12","alias_value":"FVJX3BQ6PCSD","created_at":"2026-07-05T11:44:46.518369+00:00"},{"alias_kind":"pith_short_16","alias_value":"FVJX3BQ6PCSDAKXA","created_at":"2026-07-05T11:44:46.518369+00:00"},{"alias_kind":"pith_short_8","alias_value":"FVJX3BQ6","created_at":"2026-07-05T11:44:46.518369+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20424","citing_title":"LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26616","citing_title":"Gaussian-Voxel Duet: A Dual-Scaffolding Hybrid Representation for Fast and Accurate Monocular Surface Reconstruction","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR","json":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR.json","graph_json":"https://pith.science/api/pith-number/FVJX3BQ6PCSDAKXAS57AT6J5RR/graph.json","events_json":"https://pith.science/api/pith-number/FVJX3BQ6PCSDAKXAS57AT6J5RR/events.json","paper":"https://pith.science/paper/FVJX3BQ6"},"agent_actions":{"view_html":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR","download_json":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR.json","view_paper":"https://pith.science/paper/FVJX3BQ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.10170&json=true","fetch_graph":"https://pith.science/api/pith-number/FVJX3BQ6PCSDAKXAS57AT6J5RR/graph.json","fetch_events":"https://pith.science/api/pith-number/FVJX3BQ6PCSDAKXAS57AT6J5RR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR/action/storage_attestation","attest_author":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR/action/author_attestation","sign_citation":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR/action/citation_signature","submit_replication":"https://pith.science/pith/FVJX3BQ6PCSDAKXAS57AT6J5RR/action/replication_record"}},"created_at":"2026-07-05T11:44:46.518369+00:00","updated_at":"2026-07-05T11:44:46.518369+00:00"}