{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JFFVXI4ALW4EXH4KPG4OCCLMTG","short_pith_number":"pith:JFFVXI4A","schema_version":"1.0","canonical_sha256":"494b5ba3805db84b9f8a79b8e1096c99ac8bafc835bc8a271cc4c4d56fcd2c13","source":{"kind":"arxiv","id":"2406.11672","version":3},"attestation_state":"computed","paper":{"title":"Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jaegul Choo, Jaeseong Lee, Jin-Hwa Kim, Junha Hyung, Sungwon Hwang, Susung Hong","submitted_at":"2024-06-17T15:51:59Z","abstract_excerpt":"3D reconstruction from multi-view images is one of the fundamental challenges in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising technique capable of real-time rendering with high-quality 3D reconstruction. This method utilizes 3D Gaussian representation and tile-based splatting techniques, bypassing the expensive neural field querying. Despite its potential, 3DGS encounters challenges such as needle-like artifacts, suboptimal geometries, and inaccurate normals caused by the Gaussians converging into anisotropic shapes with one dominant variance."},"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":"2406.11672","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-17T15:51:59Z","cross_cats_sorted":[],"title_canon_sha256":"913dc4832b8d4ca433fc7e5797e6f9badc786f081fbc84ffa51cac373ceae68b","abstract_canon_sha256":"cba08162b15e848f3902349095b265bdb14b7f3f4172bb5861207072c9390461"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:50.469648Z","signature_b64":"UJGAGNFXcY11vZLwVsmUaeOoDsjpLCHPd43W+9ORwKuTh9QWDSdBDtvDrkNMuCZQLgijtL1fJ0b8sdoGqjXhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"494b5ba3805db84b9f8a79b8e1096c99ac8bafc835bc8a271cc4c4d56fcd2c13","last_reissued_at":"2026-07-05T09:45:50.469187Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:50.469187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Effective Rank Analysis and Regularization for Enhanced 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jaegul Choo, Jaeseong Lee, Jin-Hwa Kim, Junha Hyung, Sungwon Hwang, Susung Hong","submitted_at":"2024-06-17T15:51:59Z","abstract_excerpt":"3D reconstruction from multi-view images is one of the fundamental challenges in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a promising technique capable of real-time rendering with high-quality 3D reconstruction. This method utilizes 3D Gaussian representation and tile-based splatting techniques, bypassing the expensive neural field querying. Despite its potential, 3DGS encounters challenges such as needle-like artifacts, suboptimal geometries, and inaccurate normals caused by the Gaussians converging into anisotropic shapes with one dominant variance."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11672","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/2406.11672/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":"2406.11672","created_at":"2026-07-05T09:45:50.469248+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.11672v3","created_at":"2026-07-05T09:45:50.469248+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11672","created_at":"2026-07-05T09:45:50.469248+00:00"},{"alias_kind":"pith_short_12","alias_value":"JFFVXI4ALW4E","created_at":"2026-07-05T09:45:50.469248+00:00"},{"alias_kind":"pith_short_16","alias_value":"JFFVXI4ALW4EXH4K","created_at":"2026-07-05T09:45:50.469248+00:00"},{"alias_kind":"pith_short_8","alias_value":"JFFVXI4A","created_at":"2026-07-05T09:45:50.469248+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.18334","citing_title":"3D Skew Gaussian Splatting with Any Camera Trajectory Visualization Engine","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG","json":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG.json","graph_json":"https://pith.science/api/pith-number/JFFVXI4ALW4EXH4KPG4OCCLMTG/graph.json","events_json":"https://pith.science/api/pith-number/JFFVXI4ALW4EXH4KPG4OCCLMTG/events.json","paper":"https://pith.science/paper/JFFVXI4A"},"agent_actions":{"view_html":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG","download_json":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG.json","view_paper":"https://pith.science/paper/JFFVXI4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.11672&json=true","fetch_graph":"https://pith.science/api/pith-number/JFFVXI4ALW4EXH4KPG4OCCLMTG/graph.json","fetch_events":"https://pith.science/api/pith-number/JFFVXI4ALW4EXH4KPG4OCCLMTG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG/action/storage_attestation","attest_author":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG/action/author_attestation","sign_citation":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG/action/citation_signature","submit_replication":"https://pith.science/pith/JFFVXI4ALW4EXH4KPG4OCCLMTG/action/replication_record"}},"created_at":"2026-07-05T09:45:50.469248+00:00","updated_at":"2026-07-05T09:45:50.469248+00:00"}