{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:6KWMX2DSJZKZ5V5NOFHT4DH5E6","short_pith_number":"pith:6KWMX2DS","schema_version":"1.0","canonical_sha256":"f2accbe8724e559ed7ad714f3e0cfd279a0bb0f40c6f9b2c79796a3705045ba1","source":{"kind":"arxiv","id":"2607.05522","version":1},"attestation_state":"computed","paper":{"title":"Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gaoxiang Jia, Junzhou Huang, Vikram Appia, Xinlei Wang","submitted_at":"2026-07-06T18:01:08Z","abstract_excerpt":"3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control. This is most limiting under sparse views or fixed acquisition budgets, where a model must identify weakly supported geometry and select informative views. We introduce a rendering-aware Bayesian 3DGS framework that tracks Gaussian geometry with a Normal-Inverse-Wishart posterior over means and covariances using renderer-derived surrogate summaries. An "},"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":"2607.05522","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-06T18:01:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7f75bd0429e2ef38e9d5d9ab1aecee8b8bcbf7dcbeb24e4e69efb858e478e3f9","abstract_canon_sha256":"2fb954e660335a55c0bbe174b476086a7e4c76b8dd440f86d69c0b2025343acb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:18:36.123631Z","signature_b64":"D2BzXQV8FVzARPUIdHph18CQTXhzlwtrz8DMl2JsNIMmrEZd9PHvQBX3QSgnkmu+bXdxaCO294FIx8aKFmGuAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2accbe8724e559ed7ad714f3e0cfd279a0bb0f40c6f9b2c79796a3705045ba1","last_reissued_at":"2026-07-08T01:18:36.123205Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:18:36.123205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Gaoxiang Jia, Junzhou Huang, Vikram Appia, Xinlei Wang","submitted_at":"2026-07-06T18:01:08Z","abstract_excerpt":"3D Gaussian splatting (3DGS) is a strong representation for real-time novel-view synthesis, but its standard training pipeline relies on point estimates and hand-tuned heuristics, providing no native uncertainty or principled complexity control. This is most limiting under sparse views or fixed acquisition budgets, where a model must identify weakly supported geometry and select informative views. We introduce a rendering-aware Bayesian 3DGS framework that tracks Gaussian geometry with a Normal-Inverse-Wishart posterior over means and covariances using renderer-derived surrogate summaries. An "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05522","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/2607.05522/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":"2607.05522","created_at":"2026-07-08T01:18:36.123266+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.05522v1","created_at":"2026-07-08T01:18:36.123266+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05522","created_at":"2026-07-08T01:18:36.123266+00:00"},{"alias_kind":"pith_short_12","alias_value":"6KWMX2DSJZKZ","created_at":"2026-07-08T01:18:36.123266+00:00"},{"alias_kind":"pith_short_16","alias_value":"6KWMX2DSJZKZ5V5N","created_at":"2026-07-08T01:18:36.123266+00:00"},{"alias_kind":"pith_short_8","alias_value":"6KWMX2DS","created_at":"2026-07-08T01:18:36.123266+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/6KWMX2DSJZKZ5V5NOFHT4DH5E6","json":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6.json","graph_json":"https://pith.science/api/pith-number/6KWMX2DSJZKZ5V5NOFHT4DH5E6/graph.json","events_json":"https://pith.science/api/pith-number/6KWMX2DSJZKZ5V5NOFHT4DH5E6/events.json","paper":"https://pith.science/paper/6KWMX2DS"},"agent_actions":{"view_html":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6","download_json":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6.json","view_paper":"https://pith.science/paper/6KWMX2DS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.05522&json=true","fetch_graph":"https://pith.science/api/pith-number/6KWMX2DSJZKZ5V5NOFHT4DH5E6/graph.json","fetch_events":"https://pith.science/api/pith-number/6KWMX2DSJZKZ5V5NOFHT4DH5E6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6/action/storage_attestation","attest_author":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6/action/author_attestation","sign_citation":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6/action/citation_signature","submit_replication":"https://pith.science/pith/6KWMX2DSJZKZ5V5NOFHT4DH5E6/action/replication_record"}},"created_at":"2026-07-08T01:18:36.123266+00:00","updated_at":"2026-07-08T01:18:36.123266+00:00"}