{"as_of":"2026-08-20T20:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8d3b1cbfb7ed448f63fc937ab47accef6cbec909346bd508588fa81978003dcf","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T15:56:41.630506Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.10842/citation-record","integrity":"/paper/2509.10842/integrity","json":"/paper/2509.10842/citation-record.json","paper":"/paper/2509.10842"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.463222Z","title":"Urban digital twins for smart cities and citizens: The case study of herrenberg, germany,","venue":null,"work_id":"a984fcf3-32f8-4950-8610-a93573988f3f","year":2020},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.334153Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:c9fa4508781dbc2ac68853cdd351e42525b41f66d05078136e1ce2038647f2a9","observation_id":"f377c321-3ae0-4418-a1d5-db33f4ceabce","resolution":{"observed_at":"2026-08-15T15:56:42.470703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.439876Z","title":"Lidar boosts 3d ecological observations and modelings: A review and perspective,","venue":null,"work_id":"da3ae668-6f74-46aa-aaea-84a0d1f5ca87","year":2020},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.340901Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:aad56c7a13f22b4eba3ba58619ea50e976533818c901dd2aeb81b43019dc443e","observation_id":"7b5a4856-8bb5-408b-ab59-a76f0b9ba626","resolution":{"observed_at":"2026-08-15T15:56:42.447379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.354370Z","title":"Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.354370Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:68c55d3dbb192dd43d32fa8f4f4adea4a8d0c31f89db64268f4bbbfaca0b5255","observation_id":"3c736ead-d292-4863-bed4-735f7023aefd","resolution":{"observed_at":"2026-08-15T15:56:41.354370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.401984Z","title":"Point cloud modeling as a bridge between landscape design and planning,","venue":null,"work_id":"38cf1af7-50ab-4c35-a0c6-e71e7b913d1d","year":2020},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.359171Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:06e07f1ec0c4573b13e2afcdfc98d633fc1d7973379f432e98d6642ee23e380f","observation_id":"3d6c267f-528c-4d60-8f23-1f90aa26f5a0","resolution":{"observed_at":"2026-08-15T15:56:42.408454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.374678Z","title":"Lidar—a technology to assist with smart cities and climate change resilience: A case study in an urban metropolis,","venue":null,"work_id":"060c6138-cc18-401e-85ff-49c5b49ab7b5","year":2018},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.365927Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:e95cecb00e743a452d5471f206ce0759e7ecd194349c55707916f63c34e21353","observation_id":"bfaad2a8-c977-493c-90b9-deb831775e06","resolution":{"observed_at":"2026-08-15T15:56:42.386454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.372579Z","title":"Deep learning for lidar point clouds in autonomous driving: A review,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.372579Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:a4a30ae897a5a1f5c708f43cdf77dadaf4b663bf3d1c8816e9c3caf3e11cd6a4","observation_id":"5bfcd06f-a025-4c32-b42d-f56c9f87cde9","resolution":{"observed_at":"2026-08-15T15:56:41.372579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.377795Z","title":"Deep learning for 3d point clouds: A survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.377795Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:38d73afc1ac42c273c6a48654be18ffe272eeaed7f124fd78caeaa1b787d7d71","observation_id":"9afd66ee-bf6a-4d42-964c-ebafb1737168","resolution":{"observed_at":"2026-08-15T15:56:41.377795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.324998Z","title":"Sqn: Weakly-supervised semantic segmentation of large- scale 3d point clouds,","venue":null,"work_id":"92b5caef-ff00-4757-bb93-5d9b73313fb7","year":2022},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.387784Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:e3addca917f336500f6625bb9a37be440aae737bcf8689f184b3801bb13b5ae5","observation_id":"f6707819-f226-42af-8ef2-f389f956c914","resolution":{"observed_at":"2026-08-15T15:56:42.330205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.392266Z","title":"3d semantic segmen- tation with submanifold sparse convolutional networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.392266Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:7a0554a388dea8a421ba7970232030aad5dc1e79784c33db7d6b7928e52dc940","observation_id":"003c7b9a-e0aa-47d8-aa7b-b9c94570699e","resolution":{"observed_at":"2026-08-15T15:56:41.392266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.398463Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.398463Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:e7c4cbb8d8f5f91f95e10282793f5ae59bf67e9f8bea63598d59a01d4ebd1303","observation_id":"d74b4663-f723-453a-8614-30672f71408c","resolution":{"observed_at":"2026-08-15T15:56:41.398463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.405599Z","title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.405599Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:7cc814e54061c26a24853862137082ece0864a8e80dd0b4a2f8ab536b8be627b","observation_id":"9f928a11-eef9-4177-9329-e1e15f92b11f","resolution":{"observed_at":"2026-08-15T15:56:41.405599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.415169Z","title":"Point transformer v3: Simpler faster stronger,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.415169Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:ac9c559b18f733016b238da7bbac51a9ef9ec04488ee41fc86edd4310a5be6f2","observation_id":"917dfad0-4f3f-459f-8095-77047d2562a2","resolution":{"observed_at":"2026-08-15T15:56:41.415169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.269084Z","title":"Randla-net: Efficient semantic segmentation of large- scale point clouds,","venue":null,"work_id":"00040841-2b9a-498a-9b2b-26b0b264d451","year":2020},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.423199Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:84db8d94b1004f47ae16784661753a4233da6fcf4cb7a04dfe1acda158f6d67c","observation_id":"2ab177d4-34b3-4a88-b23b-3df01d322c18","resolution":{"observed_at":"2026-08-15T15:56:42.273493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.03546","last_updated":"2022-04-03T03:33:43Z","snapshot_observed_at":"2026-08-01T06:14:31.660540Z","submitted_at":"2022-01-10T18:59:10Z","title":"Language-driven Semantic Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.03546","snapshot_observed_at":"2026-08-15T15:56:41.428747Z","title":"Language-driven semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.428747Z"},"links":{"cited_paper":"/paper/2201.03546","citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:035b37c58e9e32bf998c71da0cf0762a50c584492378a22acd8bb792fb9ab638","observation_id":"287922ab-b482-45b8-aacb-2770a8480897","resolution":{"observed_at":"2026-08-15T15:56:41.428747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.253261Z","title":"Scaling open-vocabulary image segmentation with image-level labels,","venue":null,"work_id":"f2198516-5bfe-45e9-b6a5-51add506159a","year":2022},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.437942Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:e756b4517c3c1b8b299c789b81851b03fea70e8d210efdfe1cd8cf17ab2ed298","observation_id":"9d6f6154-1ce4-425f-9aa3-06d5772153bc","resolution":{"observed_at":"2026-08-15T15:56:42.258574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.444349Z","title":"Maskclip: Masked self-distillation advances contrastive language-image pretraining,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.444349Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:5c54efdcc760fb5b0d5158259d364af613c08630ec22a205f118b9fd5287dd06","observation_id":"31949e6b-9199-40fc-a4c7-265d336b50b7","resolution":{"observed_at":"2026-08-15T15:56:41.444349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.225934Z","title":"Open-vocabulary semantic segmentation with mask-adapted clip,","venue":null,"work_id":"63b7e19d-be4c-4650-a0ab-fb36b78779dc","year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.449037Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:4052ec65bde04613a9e5e87f6230696d73aae67890ab2fa6bdebef3943820bb5","observation_id":"968b0aa8-c0d3-4d50-ae38-300928565a25","resolution":{"observed_at":"2026-08-15T15:56:42.230321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.454576Z","title":"Open- vocabulary panoptic segmentation with text-to-image diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.454576Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:e12b8c1af338ab498ec4e19adbff2cf20c7ce379a957b57dc5aa01b1549eb1a8","observation_id":"d99176da-0056-4a08-987f-cf98a28635ea","resolution":{"observed_at":"2026-08-15T15:56:41.454576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.460315Z","title":"Openscene: 3d scene understanding with open vocabularies,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.460315Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:f48638975128175d7895b1a1bcec87c601ac4efb0b09846cbdd12f1611f371e1","observation_id":"b5fdfeb0-817f-4d10-a5b9-4de9b91b8a03","resolution":{"observed_at":"2026-08-15T15:56:41.460315Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.194409Z","title":"Regionplc: Regional point-language contrastive learning for open-world 3d scene understand- ing,","venue":null,"work_id":"87aef5ec-071e-49c9-a96d-8ef6737b85d8","year":2024},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.464650Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:66493d67bbc59344d11ab40fc6d451880953365aa2818952a87d436d4d7c7c8c","observation_id":"b2c6eea4-c752-4c59-8828-127cfde666cb","resolution":{"observed_at":"2026-08-15T15:56:42.199083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.181255Z","title":"Pla: Language- driven open-vocabulary 3d scene understanding,","venue":null,"work_id":"d2815788-b2cc-44a9-8f3a-0f216b95538f","year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.471399Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:e620f39934fe472cd674669b4ceadd0a2050e899f8c028691e8a4eb5d6b5f32c","observation_id":"f81c5d8a-feb1-4328-b07a-93545fa36aaf","resolution":{"observed_at":"2026-08-15T15:56:42.185602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.164996Z","title":"Towards semantic segmentation of urban-scale 3d point clouds: A dataset, benchmarks and challenges,","venue":null,"work_id":"031cabb5-eec1-44cc-a4c0-acb551f72bc6","year":2021},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.477383Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:763b56f65cee8b64bcb087c1923e9591f76c44ebe8672c8dffc65ebea562c4ba","observation_id":"03f8566f-2e43-4e99-a5e2-50f90fc840ef","resolution":{"observed_at":"2026-08-15T15:56:42.170337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.149344Z","title":"Sum: A benchmark dataset of semantic urban meshes,","venue":null,"work_id":"a7332345-40b1-4b05-a2a5-bc644e9f7259","year":2021},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.482866Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:ce85502344eaa891101592cb485bd8a32d0879ea8183d203ca4744536b96e7a4","observation_id":"2474f21d-150d-4656-892c-2e876f006a90","resolution":{"observed_at":"2026-08-15T15:56:42.154852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.134150Z","title":"The hessigheim 3d (h3d) benchmark on semantic segmentation of high-resolution 3d point clouds and textured meshes from uav lidar and multi-view-stereo,","venue":null,"work_id":"923c91c3-7775-4d76-a228-efd1f49ad9a5","year":2021},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.491454Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:3524eb451d25eb3ad46f62276d6c2ae5ab28cd8aaccf9f4ec4d1e3cf46ba2437","observation_id":"5d6dc54c-5ec2-4b1f-8134-da2c1c8cbf58","resolution":{"observed_at":"2026-08-15T15:56:42.138924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.120615Z","title":"Toronto- 3d: A large-scale mobile lidar dataset for semantic segmentation of urban roadways,","venue":null,"work_id":"9e253229-2794-45cc-9275-6698ff0a0c04","year":2020},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.496711Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:80eed13cd91f5ead52d1e523e234f8c9a948e0060d31b4c7300fe8c4379f71dc","observation_id":"1725bac6-26bb-4c97-9970-1e4f7c73ca29","resolution":{"observed_at":"2026-08-15T15:56:42.125306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13631","last_updated":"2023-10-29T14:04:25Z","snapshot_observed_at":"2026-08-16T15:21:36.291027Z","submitted_at":"2023-06-23T17:36:44Z","title":"OpenMask3D: Open-Vocabulary 3D Instance Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13631","snapshot_observed_at":"2026-08-15T15:56:41.502324Z","title":"Openmask3d: Open-vocabulary 3d instance segmenta- tion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.502324Z"},"links":{"cited_paper":"/paper/2306.13631","citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:0c54ed36a1931145204c4cd88f9bd4b13aedba4f0740f41ae72bf198a9b64559","observation_id":"855bc64b-67e6-45dc-8e9b-309e3353fb16","resolution":{"observed_at":"2026-08-15T15:56:41.502324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.105722Z","title":"Open3dis: Open-vocabulary 3d instance segmentation with 2d mask guidance,","venue":null,"work_id":"0d6c51ee-5818-4c71-acd3-99df824bf8c8","year":2024},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.506656Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:30ba5b06ac759b98ded877e92c0cb950ff309cbb07f05dfd61aeac0994a2d66a","observation_id":"d239e254-bf31-4b66-b338-732f341cad79","resolution":{"observed_at":"2026-08-15T15:56:42.111348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03105","last_updated":"2023-04-12T09:22:53Z","snapshot_observed_at":"2026-08-17T13:52:25.245141Z","submitted_at":"2022-10-06T17:55:09Z","title":"Mask3D: Mask Transformer for 3D Semantic Instance Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03105","snapshot_observed_at":"2026-08-15T15:56:41.511515Z","title":"Mask3d: Mask transformer for 3d semantic instance segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.511515Z"},"links":{"cited_paper":"/paper/2210.03105","citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:c3eee889ba2fd3e82eef286bdde0634e04b597221a8398cb1b07ab3b0285d336","observation_id":"f0bc0f0f-540d-47f5-8481-b730e0b1e0e6","resolution":{"observed_at":"2026-08-15T15:56:41.511515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.518472Z","title":"Kpconv: Flexible and deformable convolution for point clouds,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.518472Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:c7f0334ad64b43006a107311ef73c9f711c86f06fb80aafde37ac9c3230fe2e0","observation_id":"ea1e491e-d283-4ee5-88aa-67b2e6e9703e","resolution":{"observed_at":"2026-08-15T15:56:41.518472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.525441Z","title":"4d spatio-temporal convnets: Minkowski convolutional neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.525441Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:776c5bcc5be8dd61c60f48051f0a7d37e63f325992e222fbfdb962572706bccb","observation_id":"16256309-c979-49a5-b8da-7c1ac239f3fc","resolution":{"observed_at":"2026-08-15T15:56:41.525441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.531083Z","title":"Point transformer,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.531083Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:53435f3ae65030b7a4037fd54d43ff1d1b54a367370ce545bdfb548c09434f4b","observation_id":"7cd2d22a-818e-4723-b5ad-4ff384860c62","resolution":{"observed_at":"2026-08-15T15:56:41.531083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:42.030239Z","title":"Point transformer v2: Grouped vector attention and partition-based pooling,","venue":null,"work_id":"fe6ee011-9d05-44b8-88b3-1d3726678ec1","year":2022},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.535768Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:aa1a13f0b670dcd5bd808a47a442f228b65a48d0b934d064223e9bb59d5c77bd","observation_id":"6e8d11f0-853d-4ef1-8a72-3439ab3f3f3a","resolution":{"observed_at":"2026-08-15T15:56:42.037045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.542885Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.542885Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:c0b97cf1e973249a5908b159fc962f5285a1cd9b362089528717ebbb6963f39b","observation_id":"00533c03-903f-4ad1-a283-36e43e2bdcc5","resolution":{"observed_at":"2026-08-15T15:56:41.542885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.548390Z","title":"Flamingo: a visual language model for few-shot learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.548390Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:a96a4fe11f1b9a2285015f016b61b6f9305727a614d5da128f9d16e0c90f98e4","observation_id":"cbb07d73-62e2-47f8-92c5-06a7165a150e","resolution":{"observed_at":"2026-08-15T15:56:41.548390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.555481Z","title":"Visual instruction tuning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.555481Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:f115a43dafc787656fb2979e0229d3221294f08f6c66dd560fb47ddcadff6d80","observation_id":"400cae78-245f-471a-a986-0c46836b76c4","resolution":{"observed_at":"2026-08-15T15:56:41.555481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.560365Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.560365Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:7cf78b0ba65981b3dad534855e2e8c60e304b96545a7f2816cd0f3368961e8e2","observation_id":"993cf14a-3122-4535-a0ef-bef9d035fc51","resolution":{"observed_at":"2026-08-15T15:56:41.560365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.969851Z","title":"Pointclip: Point cloud understanding by clip,","venue":null,"work_id":"6c4fe450-d508-46d7-986d-2a6bbc79b722","year":2022},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.567151Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:43fef322d297adb5d4a20fac83c9c143fc11c13c5844f597a47c5392b968ede1","observation_id":"6dd69d80-f0f5-4c04-9632-95079a404b63","resolution":{"observed_at":"2026-08-15T15:56:41.974606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.573307Z","title":"Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.573307Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:0ea9e55876c7b1a02cb28e264e4498b10f38fb6f6637834e74d993e8ddfb7368","observation_id":"28a3fd1e-41f1-46e6-a002-47ac0c8616c5","resolution":{"observed_at":"2026-08-15T15:56:41.573307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.942316Z","title":"Clip2point: Transfer clip to point cloud classification with 15 image-depth pre-training,","venue":null,"work_id":"613dea55-c3ac-47d7-a686-db5ff537e595","year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.579225Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:60e46fcb9684a4a571554291f6db5e1e8c68cca824d5f38fcee7730ca4ee3075","observation_id":"7ff1a54e-588b-431e-a2d5-b75e1ec362d2","resolution":{"observed_at":"2026-08-15T15:56:41.949002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.923632Z","title":"Clip2scene: Towards label-efficient 3d scene understanding by clip,","venue":null,"work_id":"c2549d88-f005-4899-8516-4f2d17a78d6c","year":2023},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.585744Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:6acfca6760183354f0d7e2f77188a8252014c76e709d82ca5b1cfa99dbe31130","observation_id":"f6d483b9-7a78-41e1-ab98-0814c713dddd","resolution":{"observed_at":"2026-08-15T15:56:41.929476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.902769Z","title":"Opengraph: Open-vocabulary hierarchical 3d graph representation in large-scale outdoor environments,","venue":null,"work_id":"00e64644-5e6e-4f5c-90b1-6de27a289ba5","year":2024},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.592307Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:ed06c9c99aca0e1d761893148a01d6f570e31f0976ee43ec147240040c3d41a0","observation_id":"7a1a7e8d-70e7-485d-83c5-80810e7d4fc0","resolution":{"observed_at":"2026-08-15T15:56:41.910642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.881912Z","title":"Openins3d: Snap and lookup for 3d open-vocabulary instance segmen- tation,","venue":null,"work_id":"91bc4817-8fb0-403e-9f22-5d619d4d0b45","year":2024},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.597332Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:d463d8d840798cc67194d449a55b34914c77362118667a270ad79200c303eb52","observation_id":"d24b16e0-c7fd-4347-9e13-34daea399d26","resolution":{"observed_at":"2026-08-15T15:56:41.888577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19782","last_updated":"2025-02-27T05:47:05Z","snapshot_observed_at":"2026-08-17T00:21:05.001521Z","submitted_at":"2025-02-27T05:47:05Z","title":"Open-Vocabulary Semantic Part Segmentation of 3D Human","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.19782","snapshot_observed_at":"2026-08-15T15:56:41.605840Z","title":"Open-vocabulary semantic part segmentation of 3d human,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.605840Z"},"links":{"cited_paper":"/paper/2502.19782","citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:487b98e67cdf54a89229f264deca9899f7d7129e8ab137b751895f286fa3ee55","observation_id":"b1d1f745-2c2e-49b2-8d23-8065ae95020d","resolution":{"observed_at":"2026-08-15T15:56:41.605840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.859099Z","title":"Large-scale point cloud semantic segmentation with superpoint graphs,","venue":null,"work_id":"4552a8b2-0ba8-4f95-a08f-9aa684fb1c9a","year":2018},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.612139Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:3ebd71fa6cd415c001e600e09c48157bf3e12ad0381e9265d32bb2fd8d42c23e","observation_id":"643e8fae-5c9b-411d-abe0-10049e15b0d3","resolution":{"observed_at":"2026-08-15T15:56:41.867691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.833049Z","title":"Large-scale point cloud semantic segmentation via local perception and global descriptor vector,","venue":null,"work_id":"89b4c5f4-af4e-4bdc-a4ca-2579ed655bcc","year":2024},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.619403Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:cdf30e7e7aa000900a4b0f045951b38e7c54e196806d34bc9d06fad9708ba9fc","observation_id":"ccb74700-6b0a-4445-b916-8694fddc4e33","resolution":{"observed_at":"2026-08-15T15:56:41.841886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.811970Z","title":"Pointnat: large- scale point cloud semantic segmentation via neighbor aggregation with transformer,","venue":null,"work_id":"999614e0-a09f-49ab-81da-5b58510193cd","year":2024},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.625573Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:7371cbf81e451f512beb03172ae02e6d357de4b02dad180bd660f020e73edb85","observation_id":"7de84c47-1059-4d4e-9bd2-9f2ae9502ac5","resolution":{"observed_at":"2026-08-15T15:56:41.818836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T15:56:41.788271Z","title":"Eyenet++: A multi-scale and multi-density approach for outdoor 3d semantic segmentation inspired by the human visual field,","venue":null,"work_id":"3eca74f4-2e1c-440e-a4b8-9c683dcbaa92","year":2025},"citing_paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:41.630506Z"},"links":{"citing_paper":"/paper/2509.10842"},"observation_digest":"sha256:24b3fa6bff3cdd4f10f46cee63b93c11a6ff612ad3baa1c6d5d6658c677a87ee","observation_id":"b893ac62-e9fe-4832-83b8-9487e684c2d1","resolution":{"observed_at":"2026-08-15T15:56:41.795943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.10842","last_updated":"2025-09-13T15:03:28Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T13:52:29.262997Z","submitted_at":"2025-09-13T15:03:28Z","title":"OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":0,"verified_fuzzy":25},"total_outbound_references":47},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2509.10842."}