{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7KZXDA3M5OM7DX6UPPVKI5ZCHJ","short_pith_number":"pith:7KZXDA3M","schema_version":"1.0","canonical_sha256":"fab371836ceb99f1dfd47beaa477223a711b1ad5939f23f4f53206f0f14aed9a","source":{"kind":"arxiv","id":"2606.30436","version":1},"attestation_state":"computed","paper":{"title":"Robust and Efficient Monocular 3D Gaussian SLAM for Kilometer-Scale Outdoor Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Beizhen Zhao, Dongxu Shen, Guanzhi Ding, Hao Wang, Sicheng Yu","submitted_at":"2026-06-29T15:12:18Z","abstract_excerpt":"Scaling monocular 3D Gaussian Splatting (3DGS) SLAM to kilometer-level outdoor environments poses two tightly coupled challenges: fragile long-term pose tracking and excessive memory overhead during large-scale mapping. In this paper, we propose KiloGS-SLAM, a highly efficient and robust monocular 3DGS-SLAM system that jointly addresses both bottlenecks. Since high-fidelity scene reconstruction fundamentally relies on drift-free camera poses, we first introduce a motion-adaptive hybrid tracking module. This module features a condition-triggered three-tier solving pipeline. It dynamically switc"},"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":"2606.30436","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-29T15:12:18Z","cross_cats_sorted":[],"title_canon_sha256":"002112719fbc56f9ffa52493d1fd63e8fe196e5afeee7617bc6db8a15032b71e","abstract_canon_sha256":"1c109e336d3230f9c2d8d76d5dd85b73c56240fd3176ed7ea78bc256a64141a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:18:15.100567Z","signature_b64":"GogljtsiXuEgIhMnk66zL+sMQByIQ5mIAlJ31wNPOKxoKnBBlOfKkpS5UQ7JAoDAe1d/BdIunvebJKL5JAr3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fab371836ceb99f1dfd47beaa477223a711b1ad5939f23f4f53206f0f14aed9a","last_reissued_at":"2026-06-30T02:18:15.099890Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:18:15.099890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust and Efficient Monocular 3D Gaussian SLAM for Kilometer-Scale Outdoor Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Beizhen Zhao, Dongxu Shen, Guanzhi Ding, Hao Wang, Sicheng Yu","submitted_at":"2026-06-29T15:12:18Z","abstract_excerpt":"Scaling monocular 3D Gaussian Splatting (3DGS) SLAM to kilometer-level outdoor environments poses two tightly coupled challenges: fragile long-term pose tracking and excessive memory overhead during large-scale mapping. In this paper, we propose KiloGS-SLAM, a highly efficient and robust monocular 3DGS-SLAM system that jointly addresses both bottlenecks. Since high-fidelity scene reconstruction fundamentally relies on drift-free camera poses, we first introduce a motion-adaptive hybrid tracking module. This module features a condition-triggered three-tier solving pipeline. It dynamically switc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30436","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/2606.30436/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":"2606.30436","created_at":"2026-06-30T02:18:15.099989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30436v1","created_at":"2026-06-30T02:18:15.099989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30436","created_at":"2026-06-30T02:18:15.099989+00:00"},{"alias_kind":"pith_short_12","alias_value":"7KZXDA3M5OM7","created_at":"2026-06-30T02:18:15.099989+00:00"},{"alias_kind":"pith_short_16","alias_value":"7KZXDA3M5OM7DX6U","created_at":"2026-06-30T02:18:15.099989+00:00"},{"alias_kind":"pith_short_8","alias_value":"7KZXDA3M","created_at":"2026-06-30T02:18:15.099989+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ","json":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ.json","graph_json":"https://pith.science/api/pith-number/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/graph.json","events_json":"https://pith.science/api/pith-number/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/events.json","paper":"https://pith.science/paper/7KZXDA3M"},"agent_actions":{"view_html":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ","download_json":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ.json","view_paper":"https://pith.science/paper/7KZXDA3M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30436&json=true","fetch_graph":"https://pith.science/api/pith-number/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/graph.json","fetch_events":"https://pith.science/api/pith-number/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/action/storage_attestation","attest_author":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/action/author_attestation","sign_citation":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/action/citation_signature","submit_replication":"https://pith.science/pith/7KZXDA3M5OM7DX6UPPVKI5ZCHJ/action/replication_record"}},"created_at":"2026-06-30T02:18:15.099989+00:00","updated_at":"2026-06-30T02:18:15.099989+00:00"}