{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IU7EJKCLNKA6ABQ26DPDX5YEYU","short_pith_number":"pith:IU7EJKCL","schema_version":"1.0","canonical_sha256":"453e44a84b6a81e0061af0de3bf704c50ae989ba710e900be3b44526db1672a5","source":{"kind":"arxiv","id":"2405.18033","version":2},"attestation_state":"computed","paper":{"title":"RT-GS2: Real-Time Generalizable Semantic Segmentation for 3D Gaussian Representations of Radiance Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrian Munteanu, Ion Giosan, Mihnea-Bogdan Jurca, Remco Royen","submitted_at":"2024-05-28T10:34:28Z","abstract_excerpt":"Gaussian Splatting has revolutionized the world of novel view synthesis by achieving high rendering performance in real-time. Recently, studies have focused on enriching these 3D representations with semantic information for downstream tasks. In this paper, we introduce RT-GS2, the first generalizable semantic segmentation method employing Gaussian Splatting. While existing Gaussian Splatting-based approaches rely on scene-specific training, RT-GS2 demonstrates the ability to generalize to unseen scenes. Our method adopts a new approach by first extracting view-independent 3D Gaussian features"},"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":"2405.18033","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-28T10:34:28Z","cross_cats_sorted":[],"title_canon_sha256":"f307903d13bad6f62cb057dbd1b624e951b62c805e0d13d6c95d083bcb3117b2","abstract_canon_sha256":"b38de88c86ebb214c0673abec17de8414adc5d90fa89da9b2da07dd6c71efe7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:01:02.405482Z","signature_b64":"rVyTX6GdWe1JmYY/EzrU7l4045+L2rWJ5zBIPnMl9+87v8zBURrSHEbDUpnWfDFoYwhNEqWNudNqhYrxmuQ1Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"453e44a84b6a81e0061af0de3bf704c50ae989ba710e900be3b44526db1672a5","last_reissued_at":"2026-07-05T09:01:02.404997Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:01:02.404997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RT-GS2: Real-Time Generalizable Semantic Segmentation for 3D Gaussian Representations of Radiance Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adrian Munteanu, Ion Giosan, Mihnea-Bogdan Jurca, Remco Royen","submitted_at":"2024-05-28T10:34:28Z","abstract_excerpt":"Gaussian Splatting has revolutionized the world of novel view synthesis by achieving high rendering performance in real-time. Recently, studies have focused on enriching these 3D representations with semantic information for downstream tasks. In this paper, we introduce RT-GS2, the first generalizable semantic segmentation method employing Gaussian Splatting. While existing Gaussian Splatting-based approaches rely on scene-specific training, RT-GS2 demonstrates the ability to generalize to unseen scenes. Our method adopts a new approach by first extracting view-independent 3D Gaussian features"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.18033","kind":"arxiv","version":2},"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/2405.18033/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":"2405.18033","created_at":"2026-07-05T09:01:02.405055+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.18033v2","created_at":"2026-07-05T09:01:02.405055+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.18033","created_at":"2026-07-05T09:01:02.405055+00:00"},{"alias_kind":"pith_short_12","alias_value":"IU7EJKCLNKA6","created_at":"2026-07-05T09:01:02.405055+00:00"},{"alias_kind":"pith_short_16","alias_value":"IU7EJKCLNKA6ABQ2","created_at":"2026-07-05T09:01:02.405055+00:00"},{"alias_kind":"pith_short_8","alias_value":"IU7EJKCL","created_at":"2026-07-05T09:01:02.405055+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2603.19834","citing_title":"Fourier Splatting: Generalized Fourier encoded primitives for scalable radiance fields","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU","json":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU.json","graph_json":"https://pith.science/api/pith-number/IU7EJKCLNKA6ABQ26DPDX5YEYU/graph.json","events_json":"https://pith.science/api/pith-number/IU7EJKCLNKA6ABQ26DPDX5YEYU/events.json","paper":"https://pith.science/paper/IU7EJKCL"},"agent_actions":{"view_html":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU","download_json":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU.json","view_paper":"https://pith.science/paper/IU7EJKCL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.18033&json=true","fetch_graph":"https://pith.science/api/pith-number/IU7EJKCLNKA6ABQ26DPDX5YEYU/graph.json","fetch_events":"https://pith.science/api/pith-number/IU7EJKCLNKA6ABQ26DPDX5YEYU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU/action/storage_attestation","attest_author":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU/action/author_attestation","sign_citation":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU/action/citation_signature","submit_replication":"https://pith.science/pith/IU7EJKCLNKA6ABQ26DPDX5YEYU/action/replication_record"}},"created_at":"2026-07-05T09:01:02.405055+00:00","updated_at":"2026-07-05T09:01:02.405055+00:00"}