{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:B5PU6IUFUMJTXPLGFQA4UEPZXF","short_pith_number":"pith:B5PU6IUF","schema_version":"1.0","canonical_sha256":"0f5f4f2285a3133bbd662c01ca11f9b94aee28f81318ed7e07a029d3ea2194b8","source":{"kind":"arxiv","id":"2407.11793","version":1},"attestation_state":"computed","paper":{"title":"Click-Gaussian: Interactive Segmentation to Any 3D Gaussians","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Hoseok Do, Hyeonseop Song, Jaechul Kim, Seokhun Choi, Taehyeong Kim","submitted_at":"2024-07-16T14:49:27Z","abstract_excerpt":"Interactive segmentation of 3D Gaussians opens a great opportunity for real-time manipulation of 3D scenes thanks to the real-time rendering capability of 3D Gaussian Splatting. However, the current methods suffer from time-consuming post-processing to deal with noisy segmentation output. Also, they struggle to provide detailed segmentation, which is important for fine-grained manipulation of 3D scenes. In this study, we propose Click-Gaussian, which learns distinguishable feature fields of two-level granularity, facilitating segmentation without time-consuming post-processing. We delve into c"},"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":"2407.11793","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-16T14:49:27Z","cross_cats_sorted":["cs.AI","cs.GR"],"title_canon_sha256":"94cd06896af8fa73040d6fe37eb7affc26052a478063076880676a0c1a8286ab","abstract_canon_sha256":"26364aa28f6bf9d0d3cf76b6f38c4bdad728ceafb43b8021445043ae64b6730e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:41.000720Z","signature_b64":"0rKALc60mcArJRkr+uk/74tio6liyeSDOcx9Tjcp04o6FmFY9qjw9u0y2G6mkWJcWsUCjn9clmBWXVGImjjUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f5f4f2285a3133bbd662c01ca11f9b94aee28f81318ed7e07a029d3ea2194b8","last_reissued_at":"2026-07-05T08:44:41.000274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:41.000274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Click-Gaussian: Interactive Segmentation to Any 3D Gaussians","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.GR"],"primary_cat":"cs.CV","authors_text":"Hoseok Do, Hyeonseop Song, Jaechul Kim, Seokhun Choi, Taehyeong Kim","submitted_at":"2024-07-16T14:49:27Z","abstract_excerpt":"Interactive segmentation of 3D Gaussians opens a great opportunity for real-time manipulation of 3D scenes thanks to the real-time rendering capability of 3D Gaussian Splatting. However, the current methods suffer from time-consuming post-processing to deal with noisy segmentation output. Also, they struggle to provide detailed segmentation, which is important for fine-grained manipulation of 3D scenes. In this study, we propose Click-Gaussian, which learns distinguishable feature fields of two-level granularity, facilitating segmentation without time-consuming post-processing. We delve into c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.11793","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/2407.11793/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":"2407.11793","created_at":"2026-07-05T08:44:41.000339+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.11793v1","created_at":"2026-07-05T08:44:41.000339+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.11793","created_at":"2026-07-05T08:44:41.000339+00:00"},{"alias_kind":"pith_short_12","alias_value":"B5PU6IUFUMJT","created_at":"2026-07-05T08:44:41.000339+00:00"},{"alias_kind":"pith_short_16","alias_value":"B5PU6IUFUMJTXPLG","created_at":"2026-07-05T08:44:41.000339+00:00"},{"alias_kind":"pith_short_8","alias_value":"B5PU6IUF","created_at":"2026-07-05T08:44:41.000339+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04945","citing_title":"STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2401.03890","citing_title":"A Survey on 3D Gaussian Splatting","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2603.08096","citing_title":"TrianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF","json":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF.json","graph_json":"https://pith.science/api/pith-number/B5PU6IUFUMJTXPLGFQA4UEPZXF/graph.json","events_json":"https://pith.science/api/pith-number/B5PU6IUFUMJTXPLGFQA4UEPZXF/events.json","paper":"https://pith.science/paper/B5PU6IUF"},"agent_actions":{"view_html":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF","download_json":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF.json","view_paper":"https://pith.science/paper/B5PU6IUF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.11793&json=true","fetch_graph":"https://pith.science/api/pith-number/B5PU6IUFUMJTXPLGFQA4UEPZXF/graph.json","fetch_events":"https://pith.science/api/pith-number/B5PU6IUFUMJTXPLGFQA4UEPZXF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF/action/storage_attestation","attest_author":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF/action/author_attestation","sign_citation":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF/action/citation_signature","submit_replication":"https://pith.science/pith/B5PU6IUFUMJTXPLGFQA4UEPZXF/action/replication_record"}},"created_at":"2026-07-05T08:44:41.000339+00:00","updated_at":"2026-07-05T08:44:41.000339+00:00"}