{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3TM3SHVDNCNU7QHYVROH6QTVEK","short_pith_number":"pith:3TM3SHVD","schema_version":"1.0","canonical_sha256":"dcd9b91ea3689b4fc0f8ac5c7f427522bc2a13bc42903483c7d99c0c106b8d72","source":{"kind":"arxiv","id":"2403.20032","version":1},"attestation_state":"computed","paper":{"title":"HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenming Wu, Jianke Zhu, Liangjun Zhang, Yilin Zhang, Zhuopeng Li","submitted_at":"2024-03-29T07:58:21Z","abstract_excerpt":"The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings. However, the previous 3DGS-based methods have limitations in urban scenes due to reliance on initial Structure-from-Motion(SfM) points and difficulties in rendering distant, sky and low-texture areas. To overcome these challenges, we propose a hybrid optimization method named HO-Gaussian, which combines a grid-based volume with the 3DGS pipeline. HO-Gaussian eliminates the dependency on SfM point initialization, allowing for rendering of urban scenes, "},"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":"2403.20032","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-29T07:58:21Z","cross_cats_sorted":[],"title_canon_sha256":"ae86cb5b7f52141e0a0b37b8234f98010be89ae7ce349ddc850624e3aedaa9d2","abstract_canon_sha256":"7b07565e4ba000ea1a1979d60651def7b3d868b085134e9446936feaf21868b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:02:12.423083Z","signature_b64":"gxaFe8ZNSosWZIhsLNL/zxkk/K/BBVnfkR09Hgvmrq+vYQMzx1UmY5OeccUl7MNJ/aT5F8uc5DqhULH0QXqQCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcd9b91ea3689b4fc0f8ac5c7f427522bc2a13bc42903483c7d99c0c106b8d72","last_reissued_at":"2026-07-05T08:02:12.422683Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:02:12.422683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenming Wu, Jianke Zhu, Liangjun Zhang, Yilin Zhang, Zhuopeng Li","submitted_at":"2024-03-29T07:58:21Z","abstract_excerpt":"The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings. However, the previous 3DGS-based methods have limitations in urban scenes due to reliance on initial Structure-from-Motion(SfM) points and difficulties in rendering distant, sky and low-texture areas. To overcome these challenges, we propose a hybrid optimization method named HO-Gaussian, which combines a grid-based volume with the 3DGS pipeline. HO-Gaussian eliminates the dependency on SfM point initialization, allowing for rendering of urban scenes, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.20032","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/2403.20032/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":"2403.20032","created_at":"2026-07-05T08:02:12.422737+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.20032v1","created_at":"2026-07-05T08:02:12.422737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.20032","created_at":"2026-07-05T08:02:12.422737+00:00"},{"alias_kind":"pith_short_12","alias_value":"3TM3SHVDNCNU","created_at":"2026-07-05T08:02:12.422737+00:00"},{"alias_kind":"pith_short_16","alias_value":"3TM3SHVDNCNU7QHY","created_at":"2026-07-05T08:02:12.422737+00:00"},{"alias_kind":"pith_short_8","alias_value":"3TM3SHVD","created_at":"2026-07-05T08:02:12.422737+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.04887","citing_title":"Momentum-GS: Momentum Gaussian Self-Distillation for High-Quality Large Scene Reconstruction","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK","json":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK.json","graph_json":"https://pith.science/api/pith-number/3TM3SHVDNCNU7QHYVROH6QTVEK/graph.json","events_json":"https://pith.science/api/pith-number/3TM3SHVDNCNU7QHYVROH6QTVEK/events.json","paper":"https://pith.science/paper/3TM3SHVD"},"agent_actions":{"view_html":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK","download_json":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK.json","view_paper":"https://pith.science/paper/3TM3SHVD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.20032&json=true","fetch_graph":"https://pith.science/api/pith-number/3TM3SHVDNCNU7QHYVROH6QTVEK/graph.json","fetch_events":"https://pith.science/api/pith-number/3TM3SHVDNCNU7QHYVROH6QTVEK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK/action/storage_attestation","attest_author":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK/action/author_attestation","sign_citation":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK/action/citation_signature","submit_replication":"https://pith.science/pith/3TM3SHVDNCNU7QHYVROH6QTVEK/action/replication_record"}},"created_at":"2026-07-05T08:02:12.422737+00:00","updated_at":"2026-07-05T08:02:12.422737+00:00"}