{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JWRWJ75WOFRQZG4V6XVMFCKJ2O","short_pith_number":"pith:JWRWJ75W","schema_version":"1.0","canonical_sha256":"4da364ffb671630c9b95f5eac28949d3a898c927ff61412c1302a3838fc9f48c","source":{"kind":"arxiv","id":"2408.06286","version":1},"attestation_state":"computed","paper":{"title":"Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingbing Ni, Jiameng Li, Jiezhang Cao, Kai Zhang, Luc Van Gool, Wenjun Zhang, Yue Shi","submitted_at":"2024-08-12T16:49:22Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) has attracted great attention in novel view synthesis because of its superior rendering efficiency and high fidelity. However, the trained Gaussians suffer from severe zooming degradation due to non-adjustable representation derived from single-scale training. Though some methods attempt to tackle this problem via post-processing techniques such as selective rendering or filtering techniques towards primitives, the scale-specific information is not involved in Gaussians. In this paper, we propose a unified optimization method to make Gaussians adaptive for arbitrar"},"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":"2408.06286","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-12T16:49:22Z","cross_cats_sorted":[],"title_canon_sha256":"dcdf17997a82f77337c532d8828413d5bc8766b438195149fb6b8f90a6428546","abstract_canon_sha256":"f7d968c452d1fecc7d8104a98f0e978282a63abc46fd98b713bdc28b28c26ded"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:38.609065Z","signature_b64":"MFXoOIdFW5j+ujwDNS+u094ywY7enAtRHZZiKwm54smYOMDItfJXo8hsvBQqVmDnvVoSDNJyUqan+l/Zl0kcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4da364ffb671630c9b95f5eac28949d3a898c927ff61412c1302a3838fc9f48c","last_reissued_at":"2026-07-05T08:54:38.608599Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:38.608599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingbing Ni, Jiameng Li, Jiezhang Cao, Kai Zhang, Luc Van Gool, Wenjun Zhang, Yue Shi","submitted_at":"2024-08-12T16:49:22Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) has attracted great attention in novel view synthesis because of its superior rendering efficiency and high fidelity. However, the trained Gaussians suffer from severe zooming degradation due to non-adjustable representation derived from single-scale training. Though some methods attempt to tackle this problem via post-processing techniques such as selective rendering or filtering techniques towards primitives, the scale-specific information is not involved in Gaussians. In this paper, we propose a unified optimization method to make Gaussians adaptive for arbitrar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.06286","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/2408.06286/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":"2408.06286","created_at":"2026-07-05T08:54:38.608657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.06286v1","created_at":"2026-07-05T08:54:38.608657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.06286","created_at":"2026-07-05T08:54:38.608657+00:00"},{"alias_kind":"pith_short_12","alias_value":"JWRWJ75WOFRQ","created_at":"2026-07-05T08:54:38.608657+00:00"},{"alias_kind":"pith_short_16","alias_value":"JWRWJ75WOFRQZG4V","created_at":"2026-07-05T08:54:38.608657+00:00"},{"alias_kind":"pith_short_8","alias_value":"JWRWJ75W","created_at":"2026-07-05T08:54:38.608657+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.23283","citing_title":"MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O","json":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O.json","graph_json":"https://pith.science/api/pith-number/JWRWJ75WOFRQZG4V6XVMFCKJ2O/graph.json","events_json":"https://pith.science/api/pith-number/JWRWJ75WOFRQZG4V6XVMFCKJ2O/events.json","paper":"https://pith.science/paper/JWRWJ75W"},"agent_actions":{"view_html":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O","download_json":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O.json","view_paper":"https://pith.science/paper/JWRWJ75W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.06286&json=true","fetch_graph":"https://pith.science/api/pith-number/JWRWJ75WOFRQZG4V6XVMFCKJ2O/graph.json","fetch_events":"https://pith.science/api/pith-number/JWRWJ75WOFRQZG4V6XVMFCKJ2O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O/action/storage_attestation","attest_author":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O/action/author_attestation","sign_citation":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O/action/citation_signature","submit_replication":"https://pith.science/pith/JWRWJ75WOFRQZG4V6XVMFCKJ2O/action/replication_record"}},"created_at":"2026-07-05T08:54:38.608657+00:00","updated_at":"2026-07-05T08:54:38.608657+00:00"}