{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CB6YBHNMLTXA7R75EGWAFXZVCO","short_pith_number":"pith:CB6YBHNM","schema_version":"1.0","canonical_sha256":"107d809dac5cee0fc7fd21ac02df3513900ede2f26d3f36a5af2260352d7db53","source":{"kind":"arxiv","id":"2607.11184","version":1},"attestation_state":"computed","paper":{"title":"GeoGS-SLAM: Online Monocular Reconstruction Using Gaussian Splatting with Geometric Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Letian Jin, Ruilan Gao, Yu Zhang","submitted_at":"2026-07-13T07:32:09Z","abstract_excerpt":"SLAM methods based on 3D Gaussian Splatting (3DGS) have demonstrated impressive tracking and mapping performance, but typically require additional geometric information from external depth sensors. Meanwhile, recent SLAM systems that leverage geometric priors from pre-trained feed-forward models enable real-time dense reconstruction, yet often discard original RGB information during optimization, thus degrading overall reconstruction quality. We present GeoGS-SLAM, an online monocular dense reconstruction system that combines the 3DGS-based map representation with learned geometric priors. Giv"},"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":"2607.11184","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-07-13T07:32:09Z","cross_cats_sorted":[],"title_canon_sha256":"0c1651f3179f7f358b6f54f77eec6539b90e46a0da08a3c4d5a90e9067578675","abstract_canon_sha256":"07d3247ccc708661d61447232c4f61981dfd5e9be2ca95e2025a439d33f67555"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:22:21.035798Z","signature_b64":"pCADuZkU+p12QYVFrcBBNTP1nyXtd0vWNJFrgxdzTj2ADAg8ZVXv6AxCp5dHPe7u5L4mJ6OBu+P9W3ItbqhFAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"107d809dac5cee0fc7fd21ac02df3513900ede2f26d3f36a5af2260352d7db53","last_reissued_at":"2026-07-14T01:22:21.034932Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:22:21.034932Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GeoGS-SLAM: Online Monocular Reconstruction Using Gaussian Splatting with Geometric Priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Letian Jin, Ruilan Gao, Yu Zhang","submitted_at":"2026-07-13T07:32:09Z","abstract_excerpt":"SLAM methods based on 3D Gaussian Splatting (3DGS) have demonstrated impressive tracking and mapping performance, but typically require additional geometric information from external depth sensors. Meanwhile, recent SLAM systems that leverage geometric priors from pre-trained feed-forward models enable real-time dense reconstruction, yet often discard original RGB information during optimization, thus degrading overall reconstruction quality. We present GeoGS-SLAM, an online monocular dense reconstruction system that combines the 3DGS-based map representation with learned geometric priors. Giv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11184","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/2607.11184/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":"2607.11184","created_at":"2026-07-14T01:22:21.035385+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.11184v1","created_at":"2026-07-14T01:22:21.035385+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11184","created_at":"2026-07-14T01:22:21.035385+00:00"},{"alias_kind":"pith_short_12","alias_value":"CB6YBHNMLTXA","created_at":"2026-07-14T01:22:21.035385+00:00"},{"alias_kind":"pith_short_16","alias_value":"CB6YBHNMLTXA7R75","created_at":"2026-07-14T01:22:21.035385+00:00"},{"alias_kind":"pith_short_8","alias_value":"CB6YBHNM","created_at":"2026-07-14T01:22:21.035385+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/CB6YBHNMLTXA7R75EGWAFXZVCO","json":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO.json","graph_json":"https://pith.science/api/pith-number/CB6YBHNMLTXA7R75EGWAFXZVCO/graph.json","events_json":"https://pith.science/api/pith-number/CB6YBHNMLTXA7R75EGWAFXZVCO/events.json","paper":"https://pith.science/paper/CB6YBHNM"},"agent_actions":{"view_html":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO","download_json":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO.json","view_paper":"https://pith.science/paper/CB6YBHNM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.11184&json=true","fetch_graph":"https://pith.science/api/pith-number/CB6YBHNMLTXA7R75EGWAFXZVCO/graph.json","fetch_events":"https://pith.science/api/pith-number/CB6YBHNMLTXA7R75EGWAFXZVCO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO/action/storage_attestation","attest_author":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO/action/author_attestation","sign_citation":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO/action/citation_signature","submit_replication":"https://pith.science/pith/CB6YBHNMLTXA7R75EGWAFXZVCO/action/replication_record"}},"created_at":"2026-07-14T01:22:21.035385+00:00","updated_at":"2026-07-14T01:22:21.035385+00:00"}