{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RTBUR4IVCH4GBS25KTYEVHYNBH","short_pith_number":"pith:RTBUR4IV","schema_version":"1.0","canonical_sha256":"8cc348f11511f860cb5d54f04a9f0d09d69b3b8d8c7df79bfc394c65a116ab07","source":{"kind":"arxiv","id":"2508.20754","version":1},"attestation_state":"computed","paper":{"title":"${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Friedrich Fraundorfer, Jun Zhang, Kuangyi Chen, Yuxi Hu, Zhe Zhang","submitted_at":"2025-08-28T13:12:18Z","abstract_excerpt":"Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization. In particular, recent advancements utilize feed-forward networks to predict per-pixel Gaussian parameters, enabling high-quality synthesis from sparse input views. However, existing approaches fall short in encoding discriminative, multi-view consistent features for Gaussian predictions, which struggle to construct accurate geometry with sparse views. To address this, we propose $\\mathbf{C}^{3}$-GS, a framework that enhances feature learning by incorporating context-aware, cross-di"},"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":"2508.20754","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-28T13:12:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"92eab2ce4e685976b70f83129214896250fed12b451f515794d17710d07d06dc","abstract_canon_sha256":"4cefff93965c6890a5014708fd21cd663d5feb58fab6ba736d875fc190e84e9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:01:10.190404Z","signature_b64":"0xGfpuUNYA8ZjGFgtF4wfL2hWVhEKbNxbJlkq2H8sNZJscIUgNMS1FYOQtTFzeffVBRcBCK3AYdbvLS2chQwAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8cc348f11511f860cb5d54f04a9f0d09d69b3b8d8c7df79bfc394c65a116ab07","last_reissued_at":"2026-07-05T12:01:10.189875Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:01:10.189875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Friedrich Fraundorfer, Jun Zhang, Kuangyi Chen, Yuxi Hu, Zhe Zhang","submitted_at":"2025-08-28T13:12:18Z","abstract_excerpt":"Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization. In particular, recent advancements utilize feed-forward networks to predict per-pixel Gaussian parameters, enabling high-quality synthesis from sparse input views. However, existing approaches fall short in encoding discriminative, multi-view consistent features for Gaussian predictions, which struggle to construct accurate geometry with sparse views. To address this, we propose $\\mathbf{C}^{3}$-GS, a framework that enhances feature learning by incorporating context-aware, cross-di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20754","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/2508.20754/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":"2508.20754","created_at":"2026-07-05T12:01:10.189940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.20754v1","created_at":"2026-07-05T12:01:10.189940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20754","created_at":"2026-07-05T12:01:10.189940+00:00"},{"alias_kind":"pith_short_12","alias_value":"RTBUR4IVCH4G","created_at":"2026-07-05T12:01:10.189940+00:00"},{"alias_kind":"pith_short_16","alias_value":"RTBUR4IVCH4GBS25","created_at":"2026-07-05T12:01:10.189940+00:00"},{"alias_kind":"pith_short_8","alias_value":"RTBUR4IV","created_at":"2026-07-05T12:01:10.189940+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/RTBUR4IVCH4GBS25KTYEVHYNBH","json":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH.json","graph_json":"https://pith.science/api/pith-number/RTBUR4IVCH4GBS25KTYEVHYNBH/graph.json","events_json":"https://pith.science/api/pith-number/RTBUR4IVCH4GBS25KTYEVHYNBH/events.json","paper":"https://pith.science/paper/RTBUR4IV"},"agent_actions":{"view_html":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH","download_json":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH.json","view_paper":"https://pith.science/paper/RTBUR4IV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.20754&json=true","fetch_graph":"https://pith.science/api/pith-number/RTBUR4IVCH4GBS25KTYEVHYNBH/graph.json","fetch_events":"https://pith.science/api/pith-number/RTBUR4IVCH4GBS25KTYEVHYNBH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH/action/storage_attestation","attest_author":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH/action/author_attestation","sign_citation":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH/action/citation_signature","submit_replication":"https://pith.science/pith/RTBUR4IVCH4GBS25KTYEVHYNBH/action/replication_record"}},"created_at":"2026-07-05T12:01:10.189940+00:00","updated_at":"2026-07-05T12:01:10.189940+00:00"}