{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UIMVZDJIZMEY566O3YEYTZHQJZ","short_pith_number":"pith:UIMVZDJI","schema_version":"1.0","canonical_sha256":"a2195c8d28cb098efbcede0989e4f04e77861f888ef64e511b0bd1df9f6a1616","source":{"kind":"arxiv","id":"2504.09048","version":2},"attestation_state":"computed","paper":{"title":"BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Yongchang Wu, Zhengxia Zou, Zhenwei Shi, Zipeng Qi","submitted_at":"2025-04-12T02:05:55Z","abstract_excerpt":"The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated remarkable potential in novel view synthesis tasks. The divide-and-conquer paradigm has enabled large-scale scene reconstruction, but significant challenges remain in scene partitioning, optimization, and merging processes. This paper introduces BlockGaussian, a novel framework incorporating a content-aware scene partition strategy and visibility-aware block optimization to achieve efficient and high-quality large-scale scene reconstruction. Specifically, our approach considers the content-complexity variation across dif"},"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":"2504.09048","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-12T02:05:55Z","cross_cats_sorted":[],"title_canon_sha256":"0f2e651655ab0b27714f5d1fe5003a0bff506ad3416f48aa528d7883a941e3d3","abstract_canon_sha256":"3b24afa2d1f43be3423de5ed72fd36b028303265e2171dd934128f8f4ab51ea8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:49:17.615222Z","signature_b64":"PJ6pFme2niLHfmsR+MZdQd9wCuSZ3XRNRVGD+SNvlsiaFuaDWd7fpLrd5h3ML4oubJXCrXt4HJ4zN1mQYq3LCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2195c8d28cb098efbcede0989e4f04e77861f888ef64e511b0bd1df9f6a1616","last_reissued_at":"2026-07-05T10:49:17.614301Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:49:17.614301Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via Adaptive Block-Based Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Yongchang Wu, Zhengxia Zou, Zhenwei Shi, Zipeng Qi","submitted_at":"2025-04-12T02:05:55Z","abstract_excerpt":"The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated remarkable potential in novel view synthesis tasks. The divide-and-conquer paradigm has enabled large-scale scene reconstruction, but significant challenges remain in scene partitioning, optimization, and merging processes. This paper introduces BlockGaussian, a novel framework incorporating a content-aware scene partition strategy and visibility-aware block optimization to achieve efficient and high-quality large-scale scene reconstruction. Specifically, our approach considers the content-complexity variation across dif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.09048","kind":"arxiv","version":2},"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/2504.09048/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":"2504.09048","created_at":"2026-07-05T10:49:17.614458+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.09048v2","created_at":"2026-07-05T10:49:17.614458+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.09048","created_at":"2026-07-05T10:49:17.614458+00:00"},{"alias_kind":"pith_short_12","alias_value":"UIMVZDJIZMEY","created_at":"2026-07-05T10:49:17.614458+00:00"},{"alias_kind":"pith_short_16","alias_value":"UIMVZDJIZMEY566O","created_at":"2026-07-05T10:49:17.614458+00:00"},{"alias_kind":"pith_short_8","alias_value":"UIMVZDJI","created_at":"2026-07-05T10:49:17.614458+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01698","citing_title":"Signal Structure-Aware Gaussian Splatting for Large-Scale Scene Reconstruction","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ","json":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ.json","graph_json":"https://pith.science/api/pith-number/UIMVZDJIZMEY566O3YEYTZHQJZ/graph.json","events_json":"https://pith.science/api/pith-number/UIMVZDJIZMEY566O3YEYTZHQJZ/events.json","paper":"https://pith.science/paper/UIMVZDJI"},"agent_actions":{"view_html":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ","download_json":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ.json","view_paper":"https://pith.science/paper/UIMVZDJI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.09048&json=true","fetch_graph":"https://pith.science/api/pith-number/UIMVZDJIZMEY566O3YEYTZHQJZ/graph.json","fetch_events":"https://pith.science/api/pith-number/UIMVZDJIZMEY566O3YEYTZHQJZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ/action/storage_attestation","attest_author":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ/action/author_attestation","sign_citation":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ/action/citation_signature","submit_replication":"https://pith.science/pith/UIMVZDJIZMEY566O3YEYTZHQJZ/action/replication_record"}},"created_at":"2026-07-05T10:49:17.614458+00:00","updated_at":"2026-07-05T10:49:17.614458+00:00"}