{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RTS62423YA5DSKUDNBCARTDICU","short_pith_number":"pith:RTS62423","schema_version":"1.0","canonical_sha256":"8ce5ed735bc03a392a83684408cc68151df49441fd7c38232d3850d5a1ee4344","source":{"kind":"arxiv","id":"2403.19549","version":3},"attestation_state":"computed","paper":{"title":"GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Erik Sandstr\\\"om, Ganlin Zhang, Luc Van Gool, Manthan Patel, Martin R. Oswald, Youmin Zhang","submitted_at":"2024-03-28T16:32:06Z","abstract_excerpt":"Recent advancements in RGB-only dense Simultaneous Localization and Mapping (SLAM) have predominantly utilized grid-based neural implicit encodings and/or struggle to efficiently realize global map and pose consistency. To this end, we propose an efficient RGB-only dense SLAM system using a flexible neural point cloud scene representation that adapts to keyframe poses and depth updates, without needing costly backpropagation. Another critical challenge of RGB-only SLAM is the lack of geometric priors. To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DS"},"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":"2403.19549","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-28T16:32:06Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"5d6793e04815003aa8ef7fbcda92adac25418d80bab54cf45dd3b58acb20e8e7","abstract_canon_sha256":"bf547bd79e04bc03513d4f07d95d3f8c8d7b0de456beb5750c98943578ddc3e7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:20.650872Z","signature_b64":"lp2kWzuyfHjsc7DkyF1C0vyJg7b1/LrSl0yYfXn/+ngFjUS6JnP2eHO2/4RVP0/IMwwxlA4KIKz486cJ5ykiBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8ce5ed735bc03a392a83684408cc68151df49441fd7c38232d3850d5a1ee4344","last_reissued_at":"2026-07-05T08:23:20.650355Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:20.650355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Erik Sandstr\\\"om, Ganlin Zhang, Luc Van Gool, Manthan Patel, Martin R. Oswald, Youmin Zhang","submitted_at":"2024-03-28T16:32:06Z","abstract_excerpt":"Recent advancements in RGB-only dense Simultaneous Localization and Mapping (SLAM) have predominantly utilized grid-based neural implicit encodings and/or struggle to efficiently realize global map and pose consistency. To this end, we propose an efficient RGB-only dense SLAM system using a flexible neural point cloud scene representation that adapts to keyframe poses and depth updates, without needing costly backpropagation. Another critical challenge of RGB-only SLAM is the lack of geometric priors. To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DS"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19549","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/2403.19549/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":"2403.19549","created_at":"2026-07-05T08:23:20.650417+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.19549v3","created_at":"2026-07-05T08:23:20.650417+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19549","created_at":"2026-07-05T08:23:20.650417+00:00"},{"alias_kind":"pith_short_12","alias_value":"RTS62423YA5D","created_at":"2026-07-05T08:23:20.650417+00:00"},{"alias_kind":"pith_short_16","alias_value":"RTS62423YA5DSKUD","created_at":"2026-07-05T08:23:20.650417+00:00"},{"alias_kind":"pith_short_8","alias_value":"RTS62423","created_at":"2026-07-05T08:23:20.650417+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07452","citing_title":"GeoGS-SLAM: Geometry-Only Gaussian Splatting for Dense Monocular SLAM","ref_index":38,"is_internal_anchor":true},{"citing_arxiv_id":"2606.03287","citing_title":"BA-T: An Iterative Transformer for Two-View Bundle Adjustment","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12774","citing_title":"WildPose: A Unified Framework for Robust Pose Estimation in the Wild","ref_index":56,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04642","citing_title":"WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU","json":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU.json","graph_json":"https://pith.science/api/pith-number/RTS62423YA5DSKUDNBCARTDICU/graph.json","events_json":"https://pith.science/api/pith-number/RTS62423YA5DSKUDNBCARTDICU/events.json","paper":"https://pith.science/paper/RTS62423"},"agent_actions":{"view_html":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU","download_json":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU.json","view_paper":"https://pith.science/paper/RTS62423","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.19549&json=true","fetch_graph":"https://pith.science/api/pith-number/RTS62423YA5DSKUDNBCARTDICU/graph.json","fetch_events":"https://pith.science/api/pith-number/RTS62423YA5DSKUDNBCARTDICU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU/action/storage_attestation","attest_author":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU/action/author_attestation","sign_citation":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU/action/citation_signature","submit_replication":"https://pith.science/pith/RTS62423YA5DSKUDNBCARTDICU/action/replication_record"}},"created_at":"2026-07-05T08:23:20.650417+00:00","updated_at":"2026-07-05T08:23:20.650417+00:00"}