{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MQDORVTNYTN6EKVU46FNC4HSND","short_pith_number":"pith:MQDORVTN","schema_version":"1.0","canonical_sha256":"6406e8d66dc4dbe22ab4e78ad170f268e2f5bcba07f96da5824f8e4227f5c59d","source":{"kind":"arxiv","id":"2412.19130","version":1},"attestation_state":"computed","paper":{"title":"MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online Multi-View Stereo","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Byeonggwon Lee, Junkyu Park, Khang Truong Giang, Soohwan Song, Sungho Jo","submitted_at":"2024-12-26T09:20:04Z","abstract_excerpt":"This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed reconstructions. Moreover, depth estimation based solely on images is often ambiguous, resulting in low-quality 3D models that lead to inaccurate renderings. To overcome these limitations, we propose a novel framewo"},"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":"2412.19130","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-26T09:20:04Z","cross_cats_sorted":[],"title_canon_sha256":"b37eaef5b661a284784c2ca19a0117e2d0744dcba604d8e78b30cce877e60f7d","abstract_canon_sha256":"2d7fbd888b0e471fa81c7ee58e6d8d396d318522fa55543c92c51636c8a6ec78"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:31.233004Z","signature_b64":"TJAomGgIaLFFg2ELwcZ7orh7brXcgEbvCt2gHgrNhLGWEjIAUl4pA0JyMYHhVDa+wdS4NpP+nulSPebUSW3AAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6406e8d66dc4dbe22ab4e78ad170f268e2f5bcba07f96da5824f8e4227f5c59d","last_reissued_at":"2026-07-05T09:54:31.232617Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:31.232617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online Multi-View Stereo","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Byeonggwon Lee, Junkyu Park, Khang Truong Giang, Soohwan Song, Sungho Jo","submitted_at":"2024-12-26T09:20:04Z","abstract_excerpt":"This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed reconstructions. Moreover, depth estimation based solely on images is often ambiguous, resulting in low-quality 3D models that lead to inaccurate renderings. To overcome these limitations, we propose a novel framewo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19130","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/2412.19130/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":"2412.19130","created_at":"2026-07-05T09:54:31.232677+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19130v1","created_at":"2026-07-05T09:54:31.232677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19130","created_at":"2026-07-05T09:54:31.232677+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQDORVTNYTN6","created_at":"2026-07-05T09:54:31.232677+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQDORVTNYTN6EKVU","created_at":"2026-07-05T09:54:31.232677+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQDORVTN","created_at":"2026-07-05T09:54:31.232677+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.04642","citing_title":"WaterSplat-SLAM: Photorealistic Monocular SLAM in Underwater Environment","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND","json":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND.json","graph_json":"https://pith.science/api/pith-number/MQDORVTNYTN6EKVU46FNC4HSND/graph.json","events_json":"https://pith.science/api/pith-number/MQDORVTNYTN6EKVU46FNC4HSND/events.json","paper":"https://pith.science/paper/MQDORVTN"},"agent_actions":{"view_html":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND","download_json":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND.json","view_paper":"https://pith.science/paper/MQDORVTN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19130&json=true","fetch_graph":"https://pith.science/api/pith-number/MQDORVTNYTN6EKVU46FNC4HSND/graph.json","fetch_events":"https://pith.science/api/pith-number/MQDORVTNYTN6EKVU46FNC4HSND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND/action/storage_attestation","attest_author":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND/action/author_attestation","sign_citation":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND/action/citation_signature","submit_replication":"https://pith.science/pith/MQDORVTNYTN6EKVU46FNC4HSND/action/replication_record"}},"created_at":"2026-07-05T09:54:31.232677+00:00","updated_at":"2026-07-05T09:54:31.232677+00:00"}