{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6MX4L56QRYAGEO34IPX646FXVH","short_pith_number":"pith:6MX4L56Q","schema_version":"1.0","canonical_sha256":"f32fc5f7d08e00623b7c43efee78b7a9f8cc246b392a582a64f9ae6f77547423","source":{"kind":"arxiv","id":"2403.09637","version":1},"attestation_state":"computed","paper":{"title":"GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Bu Jin, Chao Yang, Chengliang Zhong, Dawei Wang, Lina Liu, Meiqing Wang, Pengfei Li, Runyi Yang, Songen Gu, Xiangyu Chen, Xiaoxiao Long, Yuhang Zheng, Yupeng Zheng, Zengmao Wang, Zhen Chen","submitted_at":"2024-03-14T17:59:46Z","abstract_excerpt":"Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics. Such technology facilitates robots in executing object manipulations based on human language directives. To tackle this challenge, some research efforts have been dedicated to the development of language-embedded implicit fields. However, implicit fields (e.g. NeRF) encounter limitations due to the necessity of processing a large number of input views for reconstruction, coupled with their inherent inefficiencies in inference. Thus, we present the Gaus"},"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":"2403.09637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-03-14T17:59:46Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"9f4c4ce6fbf3827d89323c77eca218156c5766b53cf8a20d30b5b57636ac33a4","abstract_canon_sha256":"80291ef1b8b015842a75f15ff5ce1d9378508a4b7b5d0876a2e899ff8a9a8ea9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:12.523089Z","signature_b64":"ZbH/0ox2gTfnJo15xhyu/pAt/rpNJQXPt743rIK2cnixWo5gSEPCbKJXwcTagRJxzfHUj1T8btNPpCdfPUlhDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f32fc5f7d08e00623b7c43efee78b7a9f8cc246b392a582a64f9ae6f77547423","last_reissued_at":"2026-07-05T07:56:12.522618Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:12.522618Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Bu Jin, Chao Yang, Chengliang Zhong, Dawei Wang, Lina Liu, Meiqing Wang, Pengfei Li, Runyi Yang, Songen Gu, Xiangyu Chen, Xiaoxiao Long, Yuhang Zheng, Yupeng Zheng, Zengmao Wang, Zhen Chen","submitted_at":"2024-03-14T17:59:46Z","abstract_excerpt":"Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit, particularly within the domain of robotics. Such technology facilitates robots in executing object manipulations based on human language directives. To tackle this challenge, some research efforts have been dedicated to the development of language-embedded implicit fields. However, implicit fields (e.g. NeRF) encounter limitations due to the necessity of processing a large number of input views for reconstruction, coupled with their inherent inefficiencies in inference. Thus, we present the Gaus"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09637","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/2403.09637/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":"2403.09637","created_at":"2026-07-05T07:56:12.522677+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.09637v1","created_at":"2026-07-05T07:56:12.522677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09637","created_at":"2026-07-05T07:56:12.522677+00:00"},{"alias_kind":"pith_short_12","alias_value":"6MX4L56QRYAG","created_at":"2026-07-05T07:56:12.522677+00:00"},{"alias_kind":"pith_short_16","alias_value":"6MX4L56QRYAGEO34","created_at":"2026-07-05T07:56:12.522677+00:00"},{"alias_kind":"pith_short_8","alias_value":"6MX4L56Q","created_at":"2026-07-05T07:56:12.522677+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.08925","citing_title":"ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2512.17817","citing_title":"Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11144","citing_title":"Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09039","citing_title":"SeasonScapes: Learning Large-scale Re-lightable 3D Landscapes with Seasonal Variation from Sparse Webcams","ref_index":64,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08925","citing_title":"ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings","ref_index":57,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH","json":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH.json","graph_json":"https://pith.science/api/pith-number/6MX4L56QRYAGEO34IPX646FXVH/graph.json","events_json":"https://pith.science/api/pith-number/6MX4L56QRYAGEO34IPX646FXVH/events.json","paper":"https://pith.science/paper/6MX4L56Q"},"agent_actions":{"view_html":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH","download_json":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH.json","view_paper":"https://pith.science/paper/6MX4L56Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.09637&json=true","fetch_graph":"https://pith.science/api/pith-number/6MX4L56QRYAGEO34IPX646FXVH/graph.json","fetch_events":"https://pith.science/api/pith-number/6MX4L56QRYAGEO34IPX646FXVH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH/action/storage_attestation","attest_author":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH/action/author_attestation","sign_citation":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH/action/citation_signature","submit_replication":"https://pith.science/pith/6MX4L56QRYAGEO34IPX646FXVH/action/replication_record"}},"created_at":"2026-07-05T07:56:12.522677+00:00","updated_at":"2026-07-05T07:56:12.522677+00:00"}