{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BQADTZJKLU2YEL3T6FBKGPLVGO","short_pith_number":"pith:BQADTZJK","schema_version":"1.0","canonical_sha256":"0c0039e52a5d35822f73f142a33d7533a8e68885a3c317accd5805edbc865b02","source":{"kind":"arxiv","id":"2410.02103","version":4},"attestation_state":"computed","paper":{"title":"MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Xiaobiao Du, Xin Yu, Yida Wang","submitted_at":"2024-10-02T23:48:31Z","abstract_excerpt":"Recent works in volume rendering, \\textit{e.g.} NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with the help of the learned implicit neural radiance field or 3D Gaussians. Rendering on top of an explicit representation, the vanilla 3DGS and its variants deliver real-time efficiency by optimizing the parametric model with single-view supervision per iteration during training which is adopted from NeRF. Consequently, certain views are overfitted, leading to unsatisfying appearance in novel-view synthesis and imprecise 3D geometries. To solve afo"},"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":"2410.02103","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T23:48:31Z","cross_cats_sorted":[],"title_canon_sha256":"d0c31317fa1cbe03057840f7f2a6b2035cf8808f9c1ef395d60834b2e4dff72f","abstract_canon_sha256":"a857d0baba9e0ea7e6ff76bf9e371508e746c18b36d6978fc714f7c660073558"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:14:00.846685Z","signature_b64":"83mr8JGttP58Uzaf1RcGnHk7a15trHp9q2b0aF/Q36SnLy2hnPf5tbop7UBviu6dBCg1zpijGHuS2oEF4gVBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0c0039e52a5d35822f73f142a33d7533a8e68885a3c317accd5805edbc865b02","last_reissued_at":"2026-06-29T01:14:00.846122Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:14:00.846122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MVGS: Multi-view Regulated Gaussian Splatting for Novel View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Xiaobiao Du, Xin Yu, Yida Wang","submitted_at":"2024-10-02T23:48:31Z","abstract_excerpt":"Recent works in volume rendering, \\textit{e.g.} NeRF and 3D Gaussian Splatting (3DGS), significantly advance the rendering quality and efficiency with the help of the learned implicit neural radiance field or 3D Gaussians. Rendering on top of an explicit representation, the vanilla 3DGS and its variants deliver real-time efficiency by optimizing the parametric model with single-view supervision per iteration during training which is adopted from NeRF. Consequently, certain views are overfitted, leading to unsatisfying appearance in novel-view synthesis and imprecise 3D geometries. To solve afo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02103","kind":"arxiv","version":4},"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/2410.02103/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":"2410.02103","created_at":"2026-06-29T01:14:00.846221+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.02103v4","created_at":"2026-06-29T01:14:00.846221+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02103","created_at":"2026-06-29T01:14:00.846221+00:00"},{"alias_kind":"pith_short_12","alias_value":"BQADTZJKLU2Y","created_at":"2026-06-29T01:14:00.846221+00:00"},{"alias_kind":"pith_short_16","alias_value":"BQADTZJKLU2YEL3T","created_at":"2026-06-29T01:14:00.846221+00:00"},{"alias_kind":"pith_short_8","alias_value":"BQADTZJK","created_at":"2026-06-29T01:14:00.846221+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.30017","citing_title":"Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO","json":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO.json","graph_json":"https://pith.science/api/pith-number/BQADTZJKLU2YEL3T6FBKGPLVGO/graph.json","events_json":"https://pith.science/api/pith-number/BQADTZJKLU2YEL3T6FBKGPLVGO/events.json","paper":"https://pith.science/paper/BQADTZJK"},"agent_actions":{"view_html":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO","download_json":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO.json","view_paper":"https://pith.science/paper/BQADTZJK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.02103&json=true","fetch_graph":"https://pith.science/api/pith-number/BQADTZJKLU2YEL3T6FBKGPLVGO/graph.json","fetch_events":"https://pith.science/api/pith-number/BQADTZJKLU2YEL3T6FBKGPLVGO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO/action/storage_attestation","attest_author":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO/action/author_attestation","sign_citation":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO/action/citation_signature","submit_replication":"https://pith.science/pith/BQADTZJKLU2YEL3T6FBKGPLVGO/action/replication_record"}},"created_at":"2026-06-29T01:14:00.846221+00:00","updated_at":"2026-06-29T01:14:00.846221+00:00"}