{"as_of":"2026-08-09T07:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bcde7080c1b8499cdb9f4571f731c2ec56c39674f56d79c6bbbcfa1374069de2","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:08:58.323220Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2507.14596/citation-record","integrity":"/paper/2507.14596/integrity","json":"/paper/2507.14596/citation-record.json","paper":"/paper/2507.14596"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:09:04.974655Z","title":"Deep se- mantic segmentation of natural and medical images: a re- view","venue":null,"work_id":"aeea2868-d5ca-4a6b-b3a1-23efff3c4f13","year":2021},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:53.534134Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:1885ca41469ff1ceded39fafddeb01fc5a9b2e21b8471bc3dc5f7461d551209f","observation_id":"74eb8d30-323d-455e-80ab-7a4324618718","resolution":{"observed_at":"2026-08-06T16:09:05.115170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:04.770883Z","title":"Mip-nerf 360: Unbounded anti-aliased neural radiance fields","venue":null,"work_id":"97afdedb-c961-4e81-a320-7fd49b6860ba","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:53.585381Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:b3f682820d6441f2ea712f66e7d4b244744d0a8f7f7fba0f1db4e849e3ff77dc","observation_id":"dc58f244-9654-4ff1-8da8-30acf5538939","resolution":{"observed_at":"2026-08-06T16:09:04.866891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:04.540185Z","title":"Emerg- ing properties in self-supervised vision transformers","venue":null,"work_id":"80493e72-3944-48cb-b20d-4227a6526f9d","year":2021},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:53.660267Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:b3d3e09c8fff55b2e25487754c294e281d3141bc76c4bd40976a769968306a13","observation_id":"02e71251-d31b-4225-a0a9-7e6df265f2e9","resolution":{"observed_at":"2026-08-06T16:09:04.670053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16233","last_updated":"2023-05-25T16:44:51Z","snapshot_observed_at":"2026-07-06T15:33:27.761070Z","submitted_at":"2023-05-25T16:44:51Z","title":"Interactive Segment Anything NeRF with Feature Imitation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16233","snapshot_observed_at":"2026-08-06T16:08:53.848567Z","title":"Interactive segment anything nerf with fea- ture imitation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:53.848567Z"},"links":{"cited_paper":"/paper/2305.16233","citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:7883d941538debbd934fd0318ac4d329e4a2ee5c6eb7fddb2365b432da4aaf1c","observation_id":"0bcc93fa-dc7b-4e21-9ece-30a980e47cb9","resolution":{"observed_at":"2026-08-06T16:08:53.848567Z","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":"2026-08-06T16:09:04.340572Z","title":"Selective visual repre- sentations improve convergence and generalization for em- bodied ai","venue":null,"work_id":"1f476c6d-b0e0-47d1-af0d-8822006fca79","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:54.039162Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:00a9e3715e0dfd9d91a868ae8d0d823cfd92c41bfcd3de0f674d59f7ada9096e","observation_id":"9adec1f5-8268-4c5c-88b2-6ccd4fc7bc92","resolution":{"observed_at":"2026-08-06T16:09:04.434029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.03650","last_updated":"2024-04-04T17:59:08Z","snapshot_observed_at":"2026-08-05T15:20:47.552420Z","submitted_at":"2024-04-04T17:59:08Z","title":"OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.03650","snapshot_observed_at":"2026-08-06T16:08:54.172013Z","title":"Opennerf: Open set 3d neural scene segmentation with pixel- wise features and rendered novel views","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:54.172013Z"},"links":{"cited_paper":"/paper/2404.03650","citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:ca5e7911ee9d96869e7ddf582a5bcb4c2e9d4d6867d5ce06ff527d81877a95ee","observation_id":"54c1894f-1bff-4968-95c8-2503193b822d","resolution":{"observed_at":"2026-08-06T16:08:54.172013Z","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":"2026-08-06T16:09:04.143741Z","title":"Deep multi-modal object de- tection and semantic segmentation for autonomous driving: Datasets, methods, and challenges","venue":null,"work_id":"e1eac10f-b815-444b-bf82-0bd022245e57","year":2020},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:54.346773Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:b5ea94fde669420ddf04011d96ae61e717031dc9a17f1eb07d79ef8c53d6928f","observation_id":"77c11c97-f8f9-4430-b62f-baedd808dcf2","resolution":{"observed_at":"2026-08-06T16:09:04.243816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:04.073404Z","title":"Scal- ing open-vocabulary image segmentation with image-level labels","venue":null,"work_id":"01f46715-ea6f-4b19-a50e-2e07b8eb6aa6","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:54.471419Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:fd290cd28f74b9cb495ba3018e420ab1bff48ec783905bd8019216f043c5b3d1","observation_id":"b640f83a-c53f-4b1d-a010-01bcfed5ea3b","resolution":{"observed_at":"2026-08-06T16:09:04.099124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.08414","last_updated":"2022-03-16T06:08:47Z","snapshot_observed_at":"2026-08-05T19:30:04.459795Z","submitted_at":"2022-03-16T06:08:47Z","title":"Unsupervised Semantic Segmentation by Distilling Feature Correspondences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.08414","snapshot_observed_at":"2026-08-06T16:08:54.622248Z","title":"Unsupervised semantic segmentation by distilling feature correspondences","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:54.622248Z"},"links":{"cited_paper":"/paper/2203.08414","citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:04645563e1da91530830acbde2eb2b0383681f0f0432dc14dff56fda7f7a6691","observation_id":"71be1655-db79-4e37-82d5-761bef2a6f18","resolution":{"observed_at":"2026-08-06T16:08:54.622248Z","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":"2026-08-06T16:09:03.892817Z","title":"Semantic scene segmentation for robotics","venue":null,"work_id":"19263e96-aae1-4dba-8202-ef61ec3e3c79","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:54.743431Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:0b0888c170e27c428cf35e683572735739e6b91a6edfc750b23e7759d34f7541","observation_id":"a2468e32-860b-4815-8d7d-8bfcf44c7a67","resolution":{"observed_at":"2026-08-06T16:09:03.989897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:03.720145Z","title":"3d gaussian splatting for real-time radiance field rendering","venue":null,"work_id":"6f4a2b7b-451e-42bb-abc6-6222b0f4caf2","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:54.898644Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:5435899fb2d33251ecd6b2e03a1a52b862ffc669386fb76c01342f47cbc820ac","observation_id":"0a5849b9-e1e1-44d2-a720-d88a79e9a3bd","resolution":{"observed_at":"2026-08-06T16:09:03.800211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:03.539153Z","title":"Lerf: Language embedded radiance fields","venue":null,"work_id":"ec89cf1b-0ba6-40e2-abfb-db5b42e512be","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.018641Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:7b7fd4a0429e6d429c7cf81d424158ee27c68ed5ca7dc40a99e6ebb329b3a6aa","observation_id":"9fcba13d-de25-4b6d-b988-0b008c730e42","resolution":{"observed_at":"2026-08-06T16:09:03.616926Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:03.311295Z","title":"Eagle: Eigen aggregation learning for object-centric unsupervised semantic segmentation","venue":null,"work_id":"3eb9405b-21c1-4d38-90f0-1f21b75252b3","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.162775Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:e77f6a21e6ec374198a751963ea3f79b4c4822fcd1c5e2b830bea3b9355c6ca5","observation_id":"a13448f3-2b2b-4194-8cea-6d6dd9d410cf","resolution":{"observed_at":"2026-08-06T16:09:03.423060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:03.119826Z","title":"Garfield: Group anything with radiance fields","venue":null,"work_id":"93599ec1-37ac-4d01-97fd-84c5987cc259","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.316571Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:22b50ac50d06b4703a0342fc39ee052134433398467e29747b402f38cd1d27ac","observation_id":"eb416698-1bfa-4d06-be46-231e5fdfed27","resolution":{"observed_at":"2026-08-06T16:09:03.207098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:02.934581Z","title":"Panoptic segmentation","venue":null,"work_id":"87e285d9-db2c-4c5b-8324-f03b36b9bfeb","year":2019},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.458036Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:969bab79ab31914d45a7b3f396c327c004537f6950e5ca650edff679e52095ee","observation_id":"c29efdfa-69e6-4d35-906d-65e13544823b","resolution":{"observed_at":"2026-08-06T16:09:03.041605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:02.743356Z","title":"Segment anything","venue":null,"work_id":"4cf065d3-83ca-4391-8afb-5c7337770c18","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.576522Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:fc73cd846f81ce8b30fdf2cd730e782a08d33bb0ba8a686cd5b6355041b877a9","observation_id":"fdb01a5d-6c5b-4f29-97d0-d1d1cf8424fa","resolution":{"observed_at":"2026-08-06T16:09:02.849420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:55.687593Z","title":"Decomposing nerf for editing via feature field distil- lation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.687593Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:756efedecbdd1d0d047774bb221df8822dd844c7ab5dafb2c9f4b3bdc14c6d39","observation_id":"8df17ec9-2504-4125-accf-35bacbe2e9ea","resolution":{"observed_at":"2026-08-06T16:08:55.687593Z","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":"2026-08-06T16:09:02.537853Z","title":"Smooseg: smoothness prior for unsupervised semantic segmentation","venue":null,"work_id":"901a46de-8c46-4eff-97c2-af1794217f87","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.845719Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:79bc18b3181a055cf5ffcf3de0f61ee36b350945620550d8411a1cdee454870d","observation_id":"ca6f61aa-a4ca-4ffe-b800-80af76e849a7","resolution":{"observed_at":"2026-08-06T16:09:02.631101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:02.334345Z","title":"Language-driven semantic seg- mentation","venue":null,"work_id":"790ad4a8-3ebf-4917-9244-309fc435d910","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:55.977604Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:ff8bb0bb16df1310d31d64141b2e6fc20211f40861a58c724d9de1901c06aa16","observation_id":"1528fa81-63a5-48a5-b6a9-fc426524f511","resolution":{"observed_at":"2026-08-06T16:09:02.410888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:02.077826Z","title":"Acseg: Adaptive conceptualization for unsupervised semantic seg- mentation","venue":null,"work_id":"26391631-bdc0-4526-bd13-4b6e2563893f","year":null},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.105314Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:4279c1f5e5603a59f7d026fd00a194b148122a9723c6991dda65cc1b17963326","observation_id":"1f192963-594f-47f6-b78a-d553310e7f65","resolution":{"observed_at":"2026-08-06T16:09:02.179359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:01.882371Z","title":"Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision","venue":null,"work_id":"6712808a-40e2-4954-99c6-7f41934f7d77","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.230224Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:bfcfc07db5a057d94d195285220189776b25d70803b6e7e3b6fceddd95b8ccb7","observation_id":"904bd2ab-8851-4ab2-bf46-cb75fcaad295","resolution":{"observed_at":"2026-08-06T16:09:01.992498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:01.668003Z","title":"U3ds3: Unsupervised 3d semantic scene segmenta- tion","venue":null,"work_id":"25a3980b-2f76-4bdd-9300-131019d189b1","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.350477Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:d997da074c764ac9bc0cb23870fff4f003555edc5036aa3a1fec3cba6f195127","observation_id":"ac664ee8-7445-4e6c-b789-479979467eb4","resolution":{"observed_at":"2026-08-06T16:09:01.785523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:01.488778Z","title":"Weakly supervised 3d open- vocabulary segmentation","venue":null,"work_id":"95b95019-511b-4ae5-9745-e86a203f27eb","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.473397Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:e373b04cd163c24a1763a75577a0d3814cf3b7a8f48ee475a947f73332014819","observation_id":"45530ff3-4b7f-4233-9796-31aae2734ee3","resolution":{"observed_at":"2026-08-06T16:09:01.557792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:01.263969Z","title":"Sanerf-hq: Segment anything for nerf in high quality","venue":null,"work_id":"14df6796-a2d4-4a08-9429-c6fb2a00a1f1","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.595448Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:aabe7c3bd55922d82ed86d020d0b52c1e29c9483541e7b6dd02523bea087d0ae","observation_id":"d4242a47-2e6d-4eb6-b6af-ba38fd03b478","resolution":{"observed_at":"2026-08-06T16:09:01.349919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:56.707180Z","title":"Nerf: Representing scenes as neural radiance fields for view syn- thesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.707180Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:76753f89e2d3c2a6c80ef162acd997e4d029cf2375e3b7b8960f203c357ed7ae","observation_id":"15b6e334-a8ea-4210-9006-c0c6648403fc","resolution":{"observed_at":"2026-08-06T16:08:56.707180Z","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":"2026-08-06T16:09:01.019645Z","title":"Review the state-of-the-art technologies of semantic segmentation based on deep learning.Neurocomputing, 493: 626–646, 2022","venue":null,"work_id":"8da8c8ff-8e48-40ee-8d87-0c848116e38a","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.853462Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:3a4bc19cda698c8f51dd43b918b1ad9243fb9e234b235871f27288c8954d1902","observation_id":"b4093b7c-c24d-4474-8d5f-ed56da900a41","resolution":{"observed_at":"2026-08-06T16:09:01.169742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:09:00.815492Z","title":"Instant neural graphics primitives with a multires- olution hash encoding","venue":null,"work_id":"46995212-4d5e-4697-b51f-c90071994ad3","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:56.983736Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:1486c28252a3fb0d31b5405ee3d0148fb43f09003ffb99c542c82ad55e21574e","observation_id":"20b08b82-be26-420a-b3f3-08aed05579cb","resolution":{"observed_at":"2026-08-06T16:09:00.890649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-06T05:58:29.182448Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-06T16:08:57.103203Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.103203Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:77d9aec9e2a9d7b90f72c4a1520b48c5c72f2ee3b9e53861aee9c6442ee3f34b","observation_id":"31d0b8e9-b23e-413f-aa17-8a5b1bbf7855","resolution":{"observed_at":"2026-08-06T16:08:57.103203Z","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":"2026-08-06T16:09:00.515897Z","title":"Openscene: 3d scene understanding with open vocabularies","venue":null,"work_id":"64e95775-859f-48ed-ae8e-35537252efc0","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.204128Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:c7ed0707ac522547fb61bb8807db84d0776426f73660e86478dc201b8c75e492","observation_id":"acfcd4e7-218a-4754-a9d6-26fc50659eca","resolution":{"observed_at":"2026-08-06T16:09:00.664613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:57.319950Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.319950Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:1f16ce7fccd24a755f1ad1ff24c7829de45b4a3f7e7b511ce069c826b643d2fc","observation_id":"4d04caee-c948-4fb9-9ebe-3728c8123923","resolution":{"observed_at":"2026-08-06T16:08:57.319950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07931","last_updated":"2023-12-30T01:10:41Z","snapshot_observed_at":"2026-08-04T06:54:35.295863Z","submitted_at":"2023-07-27T17:59:14Z","title":"Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07931","snapshot_observed_at":"2026-08-06T16:08:57.456293Z","title":"Distilled feature fields en- able few-shot language-guided manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.456293Z"},"links":{"cited_paper":"/paper/2308.07931","citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:fe7a22e0eb6ecea0d02066ef210bdef4b5fefc2bb58cf11892754564c6469dd6","observation_id":"ad891f1a-942c-456a-b6b5-15f38f7e257d","resolution":{"observed_at":"2026-08-06T16:08:57.456293Z","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":"2026-08-06T16:09:00.235125Z","title":"Language embedded 3d gaussians for open- vocabulary scene understanding","venue":null,"work_id":"b33bf95b-2bed-4d8c-98a9-76e988472328","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.548119Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:c63c413de1e8cd6df83c1c6213083363556923f08d132473769e9ef10bde3967","observation_id":"f3843106-d10a-4bbd-8894-957536210f6b","resolution":{"observed_at":"2026-08-06T16:09:00.387610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05797","last_updated":"2019-06-13T16:29:58Z","snapshot_observed_at":"2026-08-01T13:51:16.469557Z","submitted_at":"2019-06-13T16:29:58Z","title":"The Replica Dataset: A Digital Replica of Indoor Spaces","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.05797","snapshot_observed_at":"2026-08-06T16:08:57.617577Z","title":null,"venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.617577Z"},"links":{"cited_paper":"/paper/1906.05797","citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:eb383031e420dade4ca991f9889914efb96a9bd48431b97cf1f71a74a2e6eef1","observation_id":"33481277-52dd-41dd-b250-b13aeb5f0635","resolution":{"observed_at":"2026-08-06T16:08:57.617577Z","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":"2026-08-06T16:08:59.948116Z","title":"Nerfstudio: A modular framework for neural radiance field development","venue":null,"work_id":"0ecab224-ef96-4c41-9fb5-1606217e4322","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.700163Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:a45c1c6e3947085441724c9d6872a385665bb642278c21abaa75fb05876cb349","observation_id":"8213423f-f6dd-494e-9981-ccb47cc6ca89","resolution":{"observed_at":"2026-08-06T16:09:00.072219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:59.728537Z","title":"Neural feature fusion fields: 3d distillation of self- supervised 2d image representations","venue":null,"work_id":"d5578707-7eb4-4959-b9d0-5109bc6db5e2","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.761994Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:b8f792e1d26efb27b1186bde6dbca31b80bd83d8bfc6dba92eee0fa627beba7f","observation_id":"7f652a17-66d0-46f3-90a9-af46c0f971a7","resolution":{"observed_at":"2026-08-06T16:08:59.818353Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:59.489743Z","title":"Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation","venue":null,"work_id":"66301943-eb2f-442f-aba9-f9002f1c22aa","year":2024},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.816635Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:db7c1ae8348cef55a49bf62ba0d3baa76a040891c052ff02fe06eaddbc917b77","observation_id":"ffcc8064-290e-4005-ad67-9a89f6be6de3","resolution":{"observed_at":"2026-08-06T16:08:59.613814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:59.246872Z","title":"Growsp: Unsupervised semantic segmentation of 3d point clouds","venue":null,"work_id":"320d0f1a-638d-43e1-ab0d-afa44fc7fe76","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.871781Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:d657d16e67f5c7437cafd23aa2542037ee1b2373e03339a015fd36c3cb28fd43","observation_id":"214aff3b-1df7-4b94-84e4-b5d77b537d7e","resolution":{"observed_at":"2026-08-06T16:08:59.352653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:59.036057Z","title":"Open-vocabulary uni- versal image segmentation with maskclip","venue":null,"work_id":"5cec54de-28bd-4972-8359-9d325ccbd6b7","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:57.937730Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:62b4675812c129d6cc8624d4cf9e9a9c8dbce587ccf947871260e96ab4fca574","observation_id":"68760c10-1439-4c57-ac05-0de4fbde3e14","resolution":{"observed_at":"2026-08-06T16:08:59.146655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:59.018432Z","title":"In-place scene labelling and understanding with implicit scene representation","venue":null,"work_id":"9902e3e0-592f-4f7a-890e-0b9c97505c56","year":2021},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:58.040463Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:2bf2125b58586cd98df320b4a571744aa898ef8c8ef3c44126bc2302d050ab5e","observation_id":"e73fab5e-2f0b-414a-b601-dda44037dc82","resolution":{"observed_at":"2026-08-06T16:08:59.021140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:58.867148Z","title":"Supervised semantic segmentation based on deep learning: a survey","venue":null,"work_id":"191f3716-1a04-4730-b214-be1174b510af","year":2022},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:58.119116Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:8e75fb446fdd00f41ba511d467af1c974e33c7e9f9720a1c101923f217a94388","observation_id":"90c8e0ef-1408-4ae0-96dc-5ef8d0e1a261","resolution":{"observed_at":"2026-08-06T16:08:58.938887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:58.714793Z","title":"Generalized decoding for pixel, image, and language","venue":null,"work_id":"e7fa5c0d-3b03-4aa8-8d56-9c7eac18770a","year":2023},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:58.172605Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:f5176ed91e4d4bcb353daa7927051733bec646cebd32ce108fed981751d96a3f","observation_id":"9e9677fa-e967-49b2-9dc4-b7beee16a726","resolution":{"observed_at":"2026-08-06T16:08:58.789222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:58.584215Z","title":"Additional Architecture Details Some architecture details and minor contributions have been overlooked in the main paper that we want to cover here","venue":null,"work_id":"5727dfbc-975d-4470-b1a6-dce61756ab23","year":null},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:58.248987Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:ed06b9add1c8dbb86fdcc500f7c67b2210496d7582e4dbccc8c8d6347356018c","observation_id":"7e4206fe-8354-4208-a473-f4217510642b","resolution":{"observed_at":"2026-08-06T16:08:58.659053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T16:08:58.429835Z","title":"Hyperparameters In this section, we list the used hyperparameters for our dif- ferent experiments (both quantitative and qualitative) of the article","venue":null,"work_id":"cc257287-f3f6-4018-a8b0-eab1f9e5e311","year":null},"citing_paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T16:08:58.323220Z"},"links":{"citing_paper":"/paper/2507.14596"},"observation_digest":"sha256:6ed66be4098feec8ba07210e72c14b13b9ee89e3bb28ea8a5f918d0f23769a14","observation_id":"f6b6bbe3-be80-4240-aa43-9b0b3d592d55","resolution":{"observed_at":"2026-08-06T16:08:58.510630Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.14596","last_updated":"2025-07-19T12:46:20Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T18:51:05.713293Z","submitted_at":"2025-07-19T12:46:20Z","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":33},"total_outbound_references":43},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.14596."}