{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:I2JBT766BRMGI3MSTW2VPLWHEU","short_pith_number":"pith:I2JBT766","schema_version":"1.0","canonical_sha256":"469219ffde0c58646d929db557aec72525620ec63ece21fcdbfd4bcd288dc7d5","source":{"kind":"arxiv","id":"2608.05704","version":1},"attestation_state":"computed","paper":{"title":"G$^2$ARD-GS: Geometry-Guided Anchor-Regularized Gaussian Splatting Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianming Huang, Puyuan Zhang, Wei Dong, Wenkai Ye","submitted_at":"2026-08-06T07:46:49Z","abstract_excerpt":"Dense colored LiDAR maps provide accurate city-scale geometry, but lifting them into 3D Gaussian Splatting (3DGS) retains millions of primitives, making the resulting models costly to store, transmit, render, and adapt. Aggressive primitive reduction alleviates this burden, but can remove the local surface support needed for stable novel-view synthesis and downstream geometric use. We introduce G$^2$ARD-GS, a geometry-guided distillation method that converts a dense Gaussian prior instantiated either as a training-free point-cloud lift or a trained GS model into a compact, reusable representat"},"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":"2608.05704","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-08-06T07:46:49Z","cross_cats_sorted":[],"title_canon_sha256":"b9fe094c0a29ec3b1722a50e40de262f7cb7822e2cf75e479fdb6d9f3900b79a","abstract_canon_sha256":"f244693ebe5b3fd141b55ee46144fe197d40afa3bed3fde8a6f2f9bdd9f25b2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-07T00:52:23.274484Z","signature_b64":"GVWU6YK8aGrViVk9XAdz96o43acYDuuSWn/sesHB0cKCwzGfWeHa9Vh34PPF+FoDWsbhyefJlBXHP2ir1OJSAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"469219ffde0c58646d929db557aec72525620ec63ece21fcdbfd4bcd288dc7d5","last_reissued_at":"2026-08-07T00:52:23.273066Z","signature_status":"signed_v1","first_computed_at":"2026-08-07T00:52:23.273066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"G$^2$ARD-GS: Geometry-Guided Anchor-Regularized Gaussian Splatting Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianming Huang, Puyuan Zhang, Wei Dong, Wenkai Ye","submitted_at":"2026-08-06T07:46:49Z","abstract_excerpt":"Dense colored LiDAR maps provide accurate city-scale geometry, but lifting them into 3D Gaussian Splatting (3DGS) retains millions of primitives, making the resulting models costly to store, transmit, render, and adapt. Aggressive primitive reduction alleviates this burden, but can remove the local surface support needed for stable novel-view synthesis and downstream geometric use. We introduce G$^2$ARD-GS, a geometry-guided distillation method that converts a dense Gaussian prior instantiated either as a training-free point-cloud lift or a trained GS model into a compact, reusable representat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05704","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/2608.05704/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":"2608.05704","created_at":"2026-08-07T00:52:23.274549+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.05704v1","created_at":"2026-08-07T00:52:23.274549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05704","created_at":"2026-08-07T00:52:23.274549+00:00"},{"alias_kind":"pith_short_12","alias_value":"I2JBT766BRMG","created_at":"2026-08-07T00:52:23.274549+00:00"},{"alias_kind":"pith_short_16","alias_value":"I2JBT766BRMGI3MS","created_at":"2026-08-07T00:52:23.274549+00:00"},{"alias_kind":"pith_short_8","alias_value":"I2JBT766","created_at":"2026-08-07T00:52:23.274549+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/I2JBT766BRMGI3MSTW2VPLWHEU","json":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU.json","graph_json":"https://pith.science/api/pith-number/I2JBT766BRMGI3MSTW2VPLWHEU/graph.json","events_json":"https://pith.science/api/pith-number/I2JBT766BRMGI3MSTW2VPLWHEU/events.json","paper":"https://pith.science/paper/I2JBT766"},"agent_actions":{"view_html":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU","download_json":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU.json","view_paper":"https://pith.science/paper/I2JBT766","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.05704&json=true","fetch_graph":"https://pith.science/api/pith-number/I2JBT766BRMGI3MSTW2VPLWHEU/graph.json","fetch_events":"https://pith.science/api/pith-number/I2JBT766BRMGI3MSTW2VPLWHEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU/action/storage_attestation","attest_author":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU/action/author_attestation","sign_citation":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU/action/citation_signature","submit_replication":"https://pith.science/pith/I2JBT766BRMGI3MSTW2VPLWHEU/action/replication_record"}},"created_at":"2026-08-07T00:52:23.274549+00:00","updated_at":"2026-08-07T00:52:23.274549+00:00"}