{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UA6QTLOKJZ5EDT7JHTFEYMSF6M","short_pith_number":"pith:UA6QTLOK","schema_version":"1.0","canonical_sha256":"a03d09adca4e7a41cfe93cca4c3245f313e7ff57ec244e6cf2c5135d702616f5","source":{"kind":"arxiv","id":"2407.09510","version":5},"attestation_state":"computed","paper":{"title":"3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Hilsmann, Florian Barthel, Milena T. Bagdasarian, Paul Knoll, Peter Eisert, Wieland Morgenstern, Yi-Hsin Li","submitted_at":"2024-06-17T11:43:38Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering, offering state-of-the-art performance in terms of both quality and speed. 3DGS models a scene as a collection of three-dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands. These high demands make 3DGS impractical for mobile devices or headsets, reducing its applicability in important areas of comput"},"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":"2407.09510","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-17T11:43:38Z","cross_cats_sorted":[],"title_canon_sha256":"09263bdb009db1af2bd6b57a0a95f373adbc31e3169e5c65de9414d2f2ecdb79","abstract_canon_sha256":"71195c86c9d924cd59b4062eca3b61db4a27f2d669f5291911d636d560e5c2a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:09:12.133451Z","signature_b64":"sbsqA9xCHh6bXBQiRvm0P7mRHfW2/W1oGCUfSVGL2uLKwXaCMs31GZ5U6oeOkYUidqwKMlPITuRSLtwnD/DFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a03d09adca4e7a41cfe93cca4c3245f313e7ff57ec244e6cf2c5135d702616f5","last_reissued_at":"2026-06-10T01:09:12.132348Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:09:12.132348Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anna Hilsmann, Florian Barthel, Milena T. Bagdasarian, Paul Knoll, Peter Eisert, Wieland Morgenstern, Yi-Hsin Li","submitted_at":"2024-06-17T11:43:38Z","abstract_excerpt":"3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering, offering state-of-the-art performance in terms of both quality and speed. 3DGS models a scene as a collection of three-dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands. These high demands make 3DGS impractical for mobile devices or headsets, reducing its applicability in important areas of comput"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.09510","kind":"arxiv","version":5},"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/2407.09510/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":"2407.09510","created_at":"2026-06-10T01:09:12.132511+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.09510v5","created_at":"2026-06-10T01:09:12.132511+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.09510","created_at":"2026-06-10T01:09:12.132511+00:00"},{"alias_kind":"pith_short_12","alias_value":"UA6QTLOKJZ5E","created_at":"2026-06-10T01:09:12.132511+00:00"},{"alias_kind":"pith_short_16","alias_value":"UA6QTLOKJZ5EDT7J","created_at":"2026-06-10T01:09:12.132511+00:00"},{"alias_kind":"pith_short_8","alias_value":"UA6QTLOK","created_at":"2026-06-10T01:09:12.132511+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":5,"sample":[{"citing_arxiv_id":"2508.09977","citing_title":"A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2604.22129","citing_title":"PAGaS: Pixel-Aligned 1DoF Gaussian Splatting for Depth Refinement","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2604.20046","citing_title":"Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2604.06739","citing_title":"DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2605.02086","citing_title":"GETA-3DGS: Automatic Joint Structured Pruning and Quantization for 3D Gaussian Splatting","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M","json":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M.json","graph_json":"https://pith.science/api/pith-number/UA6QTLOKJZ5EDT7JHTFEYMSF6M/graph.json","events_json":"https://pith.science/api/pith-number/UA6QTLOKJZ5EDT7JHTFEYMSF6M/events.json","paper":"https://pith.science/paper/UA6QTLOK"},"agent_actions":{"view_html":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M","download_json":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M.json","view_paper":"https://pith.science/paper/UA6QTLOK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.09510&json=true","fetch_graph":"https://pith.science/api/pith-number/UA6QTLOKJZ5EDT7JHTFEYMSF6M/graph.json","fetch_events":"https://pith.science/api/pith-number/UA6QTLOKJZ5EDT7JHTFEYMSF6M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M/action/storage_attestation","attest_author":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M/action/author_attestation","sign_citation":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M/action/citation_signature","submit_replication":"https://pith.science/pith/UA6QTLOKJZ5EDT7JHTFEYMSF6M/action/replication_record"}},"created_at":"2026-06-10T01:09:12.132511+00:00","updated_at":"2026-06-10T01:09:12.132511+00:00"}