{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QLGIHCRSM2V3BVFGCJDMSZ2ELX","short_pith_number":"pith:QLGIHCRS","schema_version":"1.0","canonical_sha256":"82cc838a3266abb0d4a61246c967445de2f8747d49c080fd5284c2977bc93ca7","source":{"kind":"arxiv","id":"2503.17491","version":1},"attestation_state":"computed","paper":{"title":"Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Emanuele Giacomini, Giorgio Grisetti, Lorenzo De Rebotti, Luca Di Giammarino, Martin R. Oswald","submitted_at":"2025-03-21T19:00:30Z","abstract_excerpt":"LiDARs provide accurate geometric measurements, making them valuable for ego-motion estimation and reconstruction tasks. Although its success, managing an accurate and lightweight representation of the environment still poses challenges. Both classic and NeRF-based solutions have to trade off accuracy over memory and processing times. In this work, we build on recent advancements in Gaussian Splatting methods to develop a novel LiDAR odometry and mapping pipeline that exclusively relies on Gaussian primitives for its scene representation. Leveraging spherical projection, we drive the refinemen"},"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":"2503.17491","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-21T19:00:30Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"20247630251b589666fb438e3c6f6eaf7b946294ed10a4f3aca0b2471f6e762a","abstract_canon_sha256":"60f1c7bb3d0b9e36b469e46eb35afadb694978134810403d02374df2bb286ca4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:10.609036Z","signature_b64":"Jox95BCUkmHbWvzovODYQgfvHffRKqhg8JV+U/gvXkTHy5oTbAwEIPmDUVa9Cux3wc1m1PmGKkMKs3cLSLjQAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82cc838a3266abb0d4a61246c967445de2f8747d49c080fd5284c2977bc93ca7","last_reissued_at":"2026-07-05T10:37:10.608334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:10.608334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Emanuele Giacomini, Giorgio Grisetti, Lorenzo De Rebotti, Luca Di Giammarino, Martin R. Oswald","submitted_at":"2025-03-21T19:00:30Z","abstract_excerpt":"LiDARs provide accurate geometric measurements, making them valuable for ego-motion estimation and reconstruction tasks. Although its success, managing an accurate and lightweight representation of the environment still poses challenges. Both classic and NeRF-based solutions have to trade off accuracy over memory and processing times. In this work, we build on recent advancements in Gaussian Splatting methods to develop a novel LiDAR odometry and mapping pipeline that exclusively relies on Gaussian primitives for its scene representation. Leveraging spherical projection, we drive the refinemen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17491","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/2503.17491/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":"2503.17491","created_at":"2026-07-05T10:37:10.608437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17491v1","created_at":"2026-07-05T10:37:10.608437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17491","created_at":"2026-07-05T10:37:10.608437+00:00"},{"alias_kind":"pith_short_12","alias_value":"QLGIHCRSM2V3","created_at":"2026-07-05T10:37:10.608437+00:00"},{"alias_kind":"pith_short_16","alias_value":"QLGIHCRSM2V3BVFG","created_at":"2026-07-05T10:37:10.608437+00:00"},{"alias_kind":"pith_short_8","alias_value":"QLGIHCRS","created_at":"2026-07-05T10:37:10.608437+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.13492","citing_title":"RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment","ref_index":21,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX","json":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX.json","graph_json":"https://pith.science/api/pith-number/QLGIHCRSM2V3BVFGCJDMSZ2ELX/graph.json","events_json":"https://pith.science/api/pith-number/QLGIHCRSM2V3BVFGCJDMSZ2ELX/events.json","paper":"https://pith.science/paper/QLGIHCRS"},"agent_actions":{"view_html":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX","download_json":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX.json","view_paper":"https://pith.science/paper/QLGIHCRS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17491&json=true","fetch_graph":"https://pith.science/api/pith-number/QLGIHCRSM2V3BVFGCJDMSZ2ELX/graph.json","fetch_events":"https://pith.science/api/pith-number/QLGIHCRSM2V3BVFGCJDMSZ2ELX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX/action/storage_attestation","attest_author":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX/action/author_attestation","sign_citation":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX/action/citation_signature","submit_replication":"https://pith.science/pith/QLGIHCRSM2V3BVFGCJDMSZ2ELX/action/replication_record"}},"created_at":"2026-07-05T10:37:10.608437+00:00","updated_at":"2026-07-05T10:37:10.608437+00:00"}