{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PT2TUZ3FHNS37AUREUA2A4H2LO","short_pith_number":"pith:PT2TUZ3F","schema_version":"1.0","canonical_sha256":"7cf53a67653b65bf82912501a070fa5ba71454ffe8d0aba87fa8979bba8d5da3","source":{"kind":"arxiv","id":"2607.28300","version":1},"attestation_state":"computed","paper":{"title":"MonoVoc: Decoupling Geometry and Semantics for Lightweight Monocular Open-Vocabulary 3D Gaussians","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hamid R. Rabiee, Morteza Abolghasemi, Pouya Ardekhani, Zahra Dehghanian","submitted_at":"2026-07-30T14:41:02Z","abstract_excerpt":"Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstructed environments using natural language. However, current 3D Gaussian frameworks are often bottlenecked by restrictive multiview capture requirements, costly scene-specific optimization, and the massive memory overhead of storing dense language features. We present a novel, training-free pipeline that fundamentally reimagines this paradigm by explicitly decoupling 3D geometric reconstruction from semantic integration. Given a standard monocul"},"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":"2607.28300","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-30T14:41:02Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b0a1444bb17c716aa9916bf4f25ba9ab1bc06105bdcfcad1455e334d9e78cc06","abstract_canon_sha256":"cb92556c286b3d631312babb6fd633b4b90764045a9e11711fcc3a2968d21d9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cf53a67653b65bf82912501a070fa5ba71454ffe8d0aba87fa8979bba8d5da3","last_reissued_at":"2026-07-31T01:37:10.820955Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:37:10.820955Z"},"graph_snapshot":{"paper":{"title":"MonoVoc: Decoupling Geometry and Semantics for Lightweight Monocular Open-Vocabulary 3D Gaussians","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hamid R. Rabiee, Morteza Abolghasemi, Pouya Ardekhani, Zahra Dehghanian","submitted_at":"2026-07-30T14:41:02Z","abstract_excerpt":"Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstructed environments using natural language. However, current 3D Gaussian frameworks are often bottlenecked by restrictive multiview capture requirements, costly scene-specific optimization, and the massive memory overhead of storing dense language features. We present a novel, training-free pipeline that fundamentally reimagines this paradigm by explicitly decoupling 3D geometric reconstruction from semantic integration. Given a standard monocul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28300","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/2607.28300/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":"2607.28300","created_at":"2026-07-31T01:37:10.824135+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.28300v1","created_at":"2026-07-31T01:37:10.824135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28300","created_at":"2026-07-31T01:37:10.824135+00:00"},{"alias_kind":"pith_short_12","alias_value":"PT2TUZ3FHNS3","created_at":"2026-07-31T01:37:10.824135+00:00"},{"alias_kind":"pith_short_16","alias_value":"PT2TUZ3FHNS37AUR","created_at":"2026-07-31T01:37:10.824135+00:00"},{"alias_kind":"pith_short_8","alias_value":"PT2TUZ3F","created_at":"2026-07-31T01:37:10.824135+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/PT2TUZ3FHNS37AUREUA2A4H2LO","json":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO.json","graph_json":"https://pith.science/api/pith-number/PT2TUZ3FHNS37AUREUA2A4H2LO/graph.json","events_json":"https://pith.science/api/pith-number/PT2TUZ3FHNS37AUREUA2A4H2LO/events.json","paper":"https://pith.science/paper/PT2TUZ3F"},"agent_actions":{"view_html":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO","download_json":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO.json","view_paper":"https://pith.science/paper/PT2TUZ3F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.28300&json=true","fetch_graph":"https://pith.science/api/pith-number/PT2TUZ3FHNS37AUREUA2A4H2LO/graph.json","fetch_events":"https://pith.science/api/pith-number/PT2TUZ3FHNS37AUREUA2A4H2LO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO/action/storage_attestation","attest_author":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO/action/author_attestation","sign_citation":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO/action/citation_signature","submit_replication":"https://pith.science/pith/PT2TUZ3FHNS37AUREUA2A4H2LO/action/replication_record"}},"created_at":"2026-07-31T01:37:10.824135+00:00","updated_at":"2026-07-31T01:37:10.824135+00:00"}