{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ODD4QKOCPTCUM3J4V2QS7Z4HSW","short_pith_number":"pith:ODD4QKOC","schema_version":"1.0","canonical_sha256":"70c7c829c27cc5466d3caea12fe7879593b0a0e38303a42005e4edba3fc697f2","source":{"kind":"arxiv","id":"2410.04680","version":4},"attestation_state":"computed","paper":{"title":"Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Aiden Swann, Boshu Lei, Kostas Daniilidis, Matthew Strong, Monroe Kennedy III, Wen Jiang","submitted_at":"2024-10-07T01:24:39Z","abstract_excerpt":"We propose a framework for active next best view and touch selection for robotic manipulators using 3D Gaussian Splatting (3DGS). 3DGS is emerging as a useful explicit 3D scene representation for robotics, as it has the ability to represent scenes in a both photorealistic and geometrically accurate manner. However, in real-world, online robotic scenes where the number of views is limited given efficiency requirements, random view selection for 3DGS becomes impractical as views are often overlapping and redundant. We address this issue by proposing an end-to-end online training and active view "},"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":"2410.04680","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-10-07T01:24:39Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"fede0108ac6586675539045690b0c219a4939768799752f3dade7fce4816b6c5","abstract_canon_sha256":"ed6328e48667841ad2f1b5b63a7fc12c9745d0faf171ff9cc7eca744ce7e8bb9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:27:16.870967Z","signature_b64":"85JcvfEOMqtKpw7v2Lawzu7zQFFb0awr95ctIl8IT5048v2IsHcJNwDuUZBrm+WKA+gPhas9UaqaksC0S6OsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70c7c829c27cc5466d3caea12fe7879593b0a0e38303a42005e4edba3fc697f2","last_reissued_at":"2026-07-05T10:27:16.870447Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:27:16.870447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Next Best Sense: Guiding Vision and Touch with FisherRF for 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Aiden Swann, Boshu Lei, Kostas Daniilidis, Matthew Strong, Monroe Kennedy III, Wen Jiang","submitted_at":"2024-10-07T01:24:39Z","abstract_excerpt":"We propose a framework for active next best view and touch selection for robotic manipulators using 3D Gaussian Splatting (3DGS). 3DGS is emerging as a useful explicit 3D scene representation for robotics, as it has the ability to represent scenes in a both photorealistic and geometrically accurate manner. However, in real-world, online robotic scenes where the number of views is limited given efficiency requirements, random view selection for 3DGS becomes impractical as views are often overlapping and redundant. We address this issue by proposing an end-to-end online training and active view "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04680","kind":"arxiv","version":4},"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/2410.04680/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":"2410.04680","created_at":"2026-07-05T10:27:16.870506+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.04680v4","created_at":"2026-07-05T10:27:16.870506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04680","created_at":"2026-07-05T10:27:16.870506+00:00"},{"alias_kind":"pith_short_12","alias_value":"ODD4QKOCPTCU","created_at":"2026-07-05T10:27:16.870506+00:00"},{"alias_kind":"pith_short_16","alias_value":"ODD4QKOCPTCUM3J4","created_at":"2026-07-05T10:27:16.870506+00:00"},{"alias_kind":"pith_short_8","alias_value":"ODD4QKOC","created_at":"2026-07-05T10:27:16.870506+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.18866","citing_title":"RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-aware Learning","ref_index":49,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW","json":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW.json","graph_json":"https://pith.science/api/pith-number/ODD4QKOCPTCUM3J4V2QS7Z4HSW/graph.json","events_json":"https://pith.science/api/pith-number/ODD4QKOCPTCUM3J4V2QS7Z4HSW/events.json","paper":"https://pith.science/paper/ODD4QKOC"},"agent_actions":{"view_html":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW","download_json":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW.json","view_paper":"https://pith.science/paper/ODD4QKOC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.04680&json=true","fetch_graph":"https://pith.science/api/pith-number/ODD4QKOCPTCUM3J4V2QS7Z4HSW/graph.json","fetch_events":"https://pith.science/api/pith-number/ODD4QKOCPTCUM3J4V2QS7Z4HSW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW/action/storage_attestation","attest_author":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW/action/author_attestation","sign_citation":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW/action/citation_signature","submit_replication":"https://pith.science/pith/ODD4QKOCPTCUM3J4V2QS7Z4HSW/action/replication_record"}},"created_at":"2026-07-05T10:27:16.870506+00:00","updated_at":"2026-07-05T10:27:16.870506+00:00"}