{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5MCWIDCZGQMQUMWUFSY42TCZ62","short_pith_number":"pith:5MCWIDCZ","schema_version":"1.0","canonical_sha256":"eb05640c5934190a32d42cb1cd4c59f6890b25175d7e222e33b3775ac8c23818","source":{"kind":"arxiv","id":"2607.18466","version":1},"attestation_state":"computed","paper":{"title":"ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Chaoli Wang, Kaiyuan Tang","submitted_at":"2026-07-20T19:35:24Z","abstract_excerpt":"Recent advances in differentiable Gaussian splatting have highlighted the potential of primitive-based approaches as alternative scene representations for interactive, high-quality, volume visualization (VolVis) of large datasets. However, the explicit nature of current primitive-based methods, combined with isolated optimization for each VolVis scene, results in redundant, non-compact representations. We present ECoNGS, an efficient compressive neural Gaussian splatting framework for VolVis scene representation. ECoNGS employs lightweight neural networks to dynamically predict implicit, edita"},"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":"2607.18466","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T19:35:24Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"372165e7468f7c446e456b9d599ad1fd124a4b5b09b6224b1c11cc1594acfafc","abstract_canon_sha256":"1df5f2727515b511661f3758a098672547e86a32da8de1082eb24053d3040a76"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T00:22:50.014428Z","signature_b64":"fzWA6sUqbQz7oKHVGJPIKzNhw4zHOk5dYuh0YFpYXywgWBJJ0Qh4fwQVIApZbdU4uEZ+LAdxfiD2Hg5px6JnAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb05640c5934190a32d42cb1cd4c59f6890b25175d7e222e33b3775ac8c23818","last_reissued_at":"2026-07-22T00:22:50.013576Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T00:22:50.013576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ECoNGS: Efficient Compressive Neural Gaussian Splats for Volume Visualization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Chaoli Wang, Kaiyuan Tang","submitted_at":"2026-07-20T19:35:24Z","abstract_excerpt":"Recent advances in differentiable Gaussian splatting have highlighted the potential of primitive-based approaches as alternative scene representations for interactive, high-quality, volume visualization (VolVis) of large datasets. However, the explicit nature of current primitive-based methods, combined with isolated optimization for each VolVis scene, results in redundant, non-compact representations. We present ECoNGS, an efficient compressive neural Gaussian splatting framework for VolVis scene representation. ECoNGS employs lightweight neural networks to dynamically predict implicit, edita"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18466","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/2607.18466/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":"2607.18466","created_at":"2026-07-22T00:22:50.014017+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.18466v1","created_at":"2026-07-22T00:22:50.014017+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18466","created_at":"2026-07-22T00:22:50.014017+00:00"},{"alias_kind":"pith_short_12","alias_value":"5MCWIDCZGQMQ","created_at":"2026-07-22T00:22:50.014017+00:00"},{"alias_kind":"pith_short_16","alias_value":"5MCWIDCZGQMQUMWU","created_at":"2026-07-22T00:22:50.014017+00:00"},{"alias_kind":"pith_short_8","alias_value":"5MCWIDCZ","created_at":"2026-07-22T00:22:50.014017+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/5MCWIDCZGQMQUMWUFSY42TCZ62","json":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62.json","graph_json":"https://pith.science/api/pith-number/5MCWIDCZGQMQUMWUFSY42TCZ62/graph.json","events_json":"https://pith.science/api/pith-number/5MCWIDCZGQMQUMWUFSY42TCZ62/events.json","paper":"https://pith.science/paper/5MCWIDCZ"},"agent_actions":{"view_html":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62","download_json":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62.json","view_paper":"https://pith.science/paper/5MCWIDCZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.18466&json=true","fetch_graph":"https://pith.science/api/pith-number/5MCWIDCZGQMQUMWUFSY42TCZ62/graph.json","fetch_events":"https://pith.science/api/pith-number/5MCWIDCZGQMQUMWUFSY42TCZ62/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/action/storage_attestation","attest_author":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/action/author_attestation","sign_citation":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/action/citation_signature","submit_replication":"https://pith.science/pith/5MCWIDCZGQMQUMWUFSY42TCZ62/action/replication_record"}},"created_at":"2026-07-22T00:22:50.014017+00:00","updated_at":"2026-07-22T00:22:50.014017+00:00"}