{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:F4SNPG5B5RUJCV4RW6SW7NM4JQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9781ca9f50e8f34b405aa6482792d73d693e627282b779cae85db3ba9be91c79","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-27T18:57:18Z","title_canon_sha256":"04ca6f1d142b6c8e60ad2b6e8acf96ff1487cb1d9ba92de951974c21911ed20b"},"schema_version":"1.0","source":{"id":"2405.17596","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17596","created_at":"2026-07-05T08:49:10Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17596v2","created_at":"2026-07-05T08:49:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17596","created_at":"2026-07-05T08:49:10Z"},{"alias_kind":"pith_short_12","alias_value":"F4SNPG5B5RUJ","created_at":"2026-07-05T08:49:10Z"},{"alias_kind":"pith_short_16","alias_value":"F4SNPG5B5RUJCV4R","created_at":"2026-07-05T08:49:10Z"},{"alias_kind":"pith_short_8","alias_value":"F4SNPG5B","created_at":"2026-07-05T08:49:10Z"}],"graph_snapshots":[{"event_id":"sha256:9009550e916842564c5533b258f22a0d5306fc7119d9bfa95abc40b1f075a16e","target":"graph","created_at":"2026-07-05T08:49:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.17596/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D open-vocabulary scene understanding, crucial for advancing augmented reality and robotic applications, involves interpreting and locating specific regions within a 3D space as directed by natural language instructions. To this end, we introduce GOI, a framework that integrates semantic features from 2D vision-language foundation models into 3D Gaussian Splatting (3DGS) and identifies 3D Gaussians of Interest using an Optimizable Semantic-space Hyperplane. Our approach includes an efficient compression method that utilizes scene priors to condense noisy high-dimensional semantic features int","authors_text":"Jianghang Lin, Liujuan Cao, Rongrong Ji, Shaohui Dai, Shengchuan Zhang, Xinyang Li, Yansong Qu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-27T18:57:18Z","title":"GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17596","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2940cba1f060463ca52fa5a48f45a7cda85f8a40eaa2dc67c3eb1c558ade7b99","target":"record","created_at":"2026-07-05T08:49:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9781ca9f50e8f34b405aa6482792d73d693e627282b779cae85db3ba9be91c79","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-27T18:57:18Z","title_canon_sha256":"04ca6f1d142b6c8e60ad2b6e8acf96ff1487cb1d9ba92de951974c21911ed20b"},"schema_version":"1.0","source":{"id":"2405.17596","kind":"arxiv","version":2}},"canonical_sha256":"2f24d79ba1ec68915791b7a56fb59c4c09d15045bbd3612ef687d583e76799a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f24d79ba1ec68915791b7a56fb59c4c09d15045bbd3612ef687d583e76799a3","first_computed_at":"2026-07-05T08:49:10.246119Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:49:10.246119Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oNfwRoZwxdzPScQC8P8IWRrT1QnYSJCjgpzt5qOvGfTzUZ9z55qAgOUUfS7xDpo8fVYou9KcwuONTaQsphqTBg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:49:10.246691Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.17596","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2940cba1f060463ca52fa5a48f45a7cda85f8a40eaa2dc67c3eb1c558ade7b99","sha256:9009550e916842564c5533b258f22a0d5306fc7119d9bfa95abc40b1f075a16e"],"state_sha256":"ed22d49c87dd94778844552ff03e7d48f45bfedea0a22dd3f55da5a872ca790f"}