{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MJC45WJRHEL4YMXOVWFDHH4LLI","short_pith_number":"pith:MJC45WJR","schema_version":"1.0","canonical_sha256":"6245ced9313917cc32eead8a339f8b5a2e20f0fdf9ee6044f414313a60370ce6","source":{"kind":"arxiv","id":"2403.15624","version":2},"attestation_state":"computed","paper":{"title":"Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huaping Liu, Jun Guo, Qing Li, Xiaojian Ma, Yue Fan","submitted_at":"2024-03-22T21:28:19Z","abstract_excerpt":"Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, with wide-ranging applications in embodied agents and augmented reality systems. Existing methods adopt neurel rendering methods as 3D representations and jointly optimize color and semantic features to achieve rendering and scene understanding simultaneously. In this paper, we introduce Semantic Gaussians, a novel open-vocabulary scene understanding approach based on 3D Gaussian Splatting. Our key idea is to distill knowledge from 2D pre-trained models to 3D Gaussians. Unlike existing methods, we desig"},"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":"2403.15624","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-22T21:28:19Z","cross_cats_sorted":[],"title_canon_sha256":"7139fbc4a4cdb3732df572a83d9838d6819259b0371ec341fdd28cec1bfa49d5","abstract_canon_sha256":"ba8b30caec715602770d5f9da25d5e9f04b6b24ed4c9c274103c84e69af56de4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:58:23.323370Z","signature_b64":"3/ZoWOW6J/GVBBwK9F7/g1EMGJUPSQaTEEXeQDts35M572tCFbH6OxkngMFgnQCCAWwPS4nE7NKoOmEl5aouBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6245ced9313917cc32eead8a339f8b5a2e20f0fdf9ee6044f414313a60370ce6","last_reissued_at":"2026-07-05T08:58:23.322903Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:58:23.322903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic Gaussians: Open-Vocabulary Scene Understanding with 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huaping Liu, Jun Guo, Qing Li, Xiaojian Ma, Yue Fan","submitted_at":"2024-03-22T21:28:19Z","abstract_excerpt":"Open-vocabulary 3D scene understanding presents a significant challenge in computer vision, with wide-ranging applications in embodied agents and augmented reality systems. Existing methods adopt neurel rendering methods as 3D representations and jointly optimize color and semantic features to achieve rendering and scene understanding simultaneously. In this paper, we introduce Semantic Gaussians, a novel open-vocabulary scene understanding approach based on 3D Gaussian Splatting. Our key idea is to distill knowledge from 2D pre-trained models to 3D Gaussians. Unlike existing methods, we desig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.15624","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/2403.15624/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":"2403.15624","created_at":"2026-07-05T08:58:23.322958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.15624v2","created_at":"2026-07-05T08:58:23.322958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.15624","created_at":"2026-07-05T08:58:23.322958+00:00"},{"alias_kind":"pith_short_12","alias_value":"MJC45WJRHEL4","created_at":"2026-07-05T08:58:23.322958+00:00"},{"alias_kind":"pith_short_16","alias_value":"MJC45WJRHEL4YMXO","created_at":"2026-07-05T08:58:23.322958+00:00"},{"alias_kind":"pith_short_8","alias_value":"MJC45WJR","created_at":"2026-07-05T08:58:23.322958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01633","citing_title":"Bridging 3D Gaussians and Semantic Occupancy for Comprehensive Open-Vocabulary Scene Understanding from Unposed Images","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08980","citing_title":"EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04945","citing_title":"STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03314","citing_title":"TASE: Truncation-Aware Semantic Embeddings for 3D Scene Understanding and Editing","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.29376","citing_title":"SAD-GS: Learning Reliable 3D Semantic Gaussian Fields via Dynamic Geo-Semantic Anchoring","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2508.09977","citing_title":"A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation","ref_index":86,"is_internal_anchor":false},{"citing_arxiv_id":"2602.21105","citing_title":"BrepGaussian: CAD reconstruction from Multi-View Images with Gaussian Splatting","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05070","citing_title":"Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI","json":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI.json","graph_json":"https://pith.science/api/pith-number/MJC45WJRHEL4YMXOVWFDHH4LLI/graph.json","events_json":"https://pith.science/api/pith-number/MJC45WJRHEL4YMXOVWFDHH4LLI/events.json","paper":"https://pith.science/paper/MJC45WJR"},"agent_actions":{"view_html":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI","download_json":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI.json","view_paper":"https://pith.science/paper/MJC45WJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.15624&json=true","fetch_graph":"https://pith.science/api/pith-number/MJC45WJRHEL4YMXOVWFDHH4LLI/graph.json","fetch_events":"https://pith.science/api/pith-number/MJC45WJRHEL4YMXOVWFDHH4LLI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI/action/storage_attestation","attest_author":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI/action/author_attestation","sign_citation":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI/action/citation_signature","submit_replication":"https://pith.science/pith/MJC45WJRHEL4YMXOVWFDHH4LLI/action/replication_record"}},"created_at":"2026-07-05T08:58:23.322958+00:00","updated_at":"2026-07-05T08:58:23.322958+00:00"}