{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3LTBF6OOIMLRXTLVO7PJ63RMFW","short_pith_number":"pith:3LTBF6OO","schema_version":"1.0","canonical_sha256":"dae612f9ce43171bcd7577de9f6e2c2d8c3147debb2e5cb196f8b0e0761a47da","source":{"kind":"arxiv","id":"2506.07917","version":4},"attestation_state":"computed","paper":{"title":"SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Alex Hanson, Allen Tu, Haiyang Ying, Matthias Zwicker, Tom Goldstein, Yonghan Lee","submitted_at":"2025-06-09T16:30:48Z","abstract_excerpt":"Dynamic extensions of 3D Gaussian Splatting (3DGS) achieve high-quality reconstructions through neural motion fields, but per-Gaussian neural inference makes these models computationally expensive. Building on DeformableGS, we introduce Speedy Deformable 3D Gaussian Splatting (SpeeDe3DGS), which bridges this efficiency-fidelity gap through three complementary modules: Temporal Sensitivity Pruning (TSP) removes low-impact Gaussians via temporally aggregated sensitivity analysis, Temporal Sensitivity Sampling (TSS) perturbs timestamps to suppress floaters and improve temporal coherence, and Grou"},"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":"2506.07917","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GR","submitted_at":"2025-06-09T16:30:48Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"446437855f4ea60d35f77fc084738826d0cb56caae82f1901c921f63b507ae72","abstract_canon_sha256":"3a7b706b3ca055335f16b0203cb10c9552d12f7deb8f3ba6055978fad37aa41c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:30.025649Z","signature_b64":"8gNE3JiT9/e1yxdT69JWvYkUbydWc3nvB367Zs4YyLQrmZgd+Qybh80mjzvJHL7s3UzQfd/c+6oRiX4MzQVpBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dae612f9ce43171bcd7577de9f6e2c2d8c3147debb2e5cb196f8b0e0761a47da","last_reissued_at":"2026-06-19T16:10:30.025238Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:30.025238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SpeeDe3DGS: Speedy Deformable 3D Gaussian Splatting with Temporal Pruning and Motion Grouping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Alex Hanson, Allen Tu, Haiyang Ying, Matthias Zwicker, Tom Goldstein, Yonghan Lee","submitted_at":"2025-06-09T16:30:48Z","abstract_excerpt":"Dynamic extensions of 3D Gaussian Splatting (3DGS) achieve high-quality reconstructions through neural motion fields, but per-Gaussian neural inference makes these models computationally expensive. Building on DeformableGS, we introduce Speedy Deformable 3D Gaussian Splatting (SpeeDe3DGS), which bridges this efficiency-fidelity gap through three complementary modules: Temporal Sensitivity Pruning (TSP) removes low-impact Gaussians via temporally aggregated sensitivity analysis, Temporal Sensitivity Sampling (TSS) perturbs timestamps to suppress floaters and improve temporal coherence, and Grou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07917","kind":"arxiv","version":4},"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/2506.07917/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":"2506.07917","created_at":"2026-06-19T16:10:30.025294+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.07917v4","created_at":"2026-06-19T16:10:30.025294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07917","created_at":"2026-06-19T16:10:30.025294+00:00"},{"alias_kind":"pith_short_12","alias_value":"3LTBF6OOIMLR","created_at":"2026-06-19T16:10:30.025294+00:00"},{"alias_kind":"pith_short_16","alias_value":"3LTBF6OOIMLRXTLV","created_at":"2026-06-19T16:10:30.025294+00:00"},{"alias_kind":"pith_short_8","alias_value":"3LTBF6OO","created_at":"2026-06-19T16:10:30.025294+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/3LTBF6OOIMLRXTLVO7PJ63RMFW","json":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW.json","graph_json":"https://pith.science/api/pith-number/3LTBF6OOIMLRXTLVO7PJ63RMFW/graph.json","events_json":"https://pith.science/api/pith-number/3LTBF6OOIMLRXTLVO7PJ63RMFW/events.json","paper":"https://pith.science/paper/3LTBF6OO"},"agent_actions":{"view_html":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW","download_json":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW.json","view_paper":"https://pith.science/paper/3LTBF6OO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.07917&json=true","fetch_graph":"https://pith.science/api/pith-number/3LTBF6OOIMLRXTLVO7PJ63RMFW/graph.json","fetch_events":"https://pith.science/api/pith-number/3LTBF6OOIMLRXTLVO7PJ63RMFW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW/action/storage_attestation","attest_author":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW/action/author_attestation","sign_citation":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW/action/citation_signature","submit_replication":"https://pith.science/pith/3LTBF6OOIMLRXTLVO7PJ63RMFW/action/replication_record"}},"created_at":"2026-06-19T16:10:30.025294+00:00","updated_at":"2026-06-19T16:10:30.025294+00:00"}