{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CZQGIRGYEQ5OBEJUVT7P3RB6OF","short_pith_number":"pith:CZQGIRGY","schema_version":"1.0","canonical_sha256":"16606444d8243ae09134acfefdc43e71629101d443064e5b26109e2bc4a95500","source":{"kind":"arxiv","id":"2506.22973","version":1},"attestation_state":"computed","paper":{"title":"Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"AmirHossein Naghi Razlighi, Elaheh Badali Golezani, Shohreh Kasaei","submitted_at":"2025-06-28T18:11:30Z","abstract_excerpt":"3D Gaussian Splatting enables high-quality real-time rendering but often produces millions of splats, resulting in excessive storage and computational overhead. We propose a novel lossy compression method based on learnable confidence scores modeled as Beta distributions. Each splat's confidence is optimized through reconstruction-aware losses, enabling pruning of low-confidence splats while preserving visual fidelity. The proposed approach is architecture-agnostic and can be applied to any Gaussian Splatting variant. In addition, the average confidence values serve as a new metric to assess t"},"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":"2506.22973","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.GR","submitted_at":"2025-06-28T18:11:30Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5beaa9e012108eff299aab2c945383fdad84298b819b4a6d625a45e71f1bd4e0","abstract_canon_sha256":"715cc0a35466e1c87132115b16a40e2b0c426b801730c62ffe76d2915b0f3c44"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:48.571013Z","signature_b64":"JZztYj/A6yT5v8OH+Bsqh/GekK/+XhC10uaO23XmzT76al31QEqkdphzu62V+1cZuaMfNawujlKlghZSV42zCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16606444d8243ae09134acfefdc43e71629101d443064e5b26109e2bc4a95500","last_reissued_at":"2026-07-05T11:28:48.570433Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:48.570433Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Confident Splatting: Confidence-Based Compression of 3D Gaussian Splatting via Learnable Beta Distributions","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"AmirHossein Naghi Razlighi, Elaheh Badali Golezani, Shohreh Kasaei","submitted_at":"2025-06-28T18:11:30Z","abstract_excerpt":"3D Gaussian Splatting enables high-quality real-time rendering but often produces millions of splats, resulting in excessive storage and computational overhead. We propose a novel lossy compression method based on learnable confidence scores modeled as Beta distributions. Each splat's confidence is optimized through reconstruction-aware losses, enabling pruning of low-confidence splats while preserving visual fidelity. The proposed approach is architecture-agnostic and can be applied to any Gaussian Splatting variant. In addition, the average confidence values serve as a new metric to assess t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.22973","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/2506.22973/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":"2506.22973","created_at":"2026-07-05T11:28:48.570508+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.22973v1","created_at":"2026-07-05T11:28:48.570508+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.22973","created_at":"2026-07-05T11:28:48.570508+00:00"},{"alias_kind":"pith_short_12","alias_value":"CZQGIRGYEQ5O","created_at":"2026-07-05T11:28:48.570508+00:00"},{"alias_kind":"pith_short_16","alias_value":"CZQGIRGYEQ5OBEJU","created_at":"2026-07-05T11:28:48.570508+00:00"},{"alias_kind":"pith_short_8","alias_value":"CZQGIRGY","created_at":"2026-07-05T11:28:48.570508+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/CZQGIRGYEQ5OBEJUVT7P3RB6OF","json":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF.json","graph_json":"https://pith.science/api/pith-number/CZQGIRGYEQ5OBEJUVT7P3RB6OF/graph.json","events_json":"https://pith.science/api/pith-number/CZQGIRGYEQ5OBEJUVT7P3RB6OF/events.json","paper":"https://pith.science/paper/CZQGIRGY"},"agent_actions":{"view_html":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF","download_json":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF.json","view_paper":"https://pith.science/paper/CZQGIRGY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.22973&json=true","fetch_graph":"https://pith.science/api/pith-number/CZQGIRGYEQ5OBEJUVT7P3RB6OF/graph.json","fetch_events":"https://pith.science/api/pith-number/CZQGIRGYEQ5OBEJUVT7P3RB6OF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF/action/storage_attestation","attest_author":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF/action/author_attestation","sign_citation":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF/action/citation_signature","submit_replication":"https://pith.science/pith/CZQGIRGYEQ5OBEJUVT7P3RB6OF/action/replication_record"}},"created_at":"2026-07-05T11:28:48.570508+00:00","updated_at":"2026-07-05T11:28:48.570508+00:00"}