{"as_of":"2026-08-17T15:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:89debd0985526760db251d5d656c674601a8a1644ffe97c62776609d27d1fdda","coverage":[{"denominator":103,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:47:41.069280Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.11516/citation-record","integrity":"/paper/2505.11516/integrity","json":"/paper/2505.11516/citation-record.json","paper":"/paper/2505.11516"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T23:47:40.640531Z","title":"nuScenes: A multimodal dataset for autonomous driving,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.640531Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:a16c5e9fef1933493200f48e30f727e691a672b75cb03cbdcfe5025c1f75af18","observation_id":"322b11a5-3505-4851-b422-ca601c7461f1","resolution":{"observed_at":"2026-08-15T23:47:40.640531Z","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":"2026-08-15T23:47:40.645948Z","title":"Two faces of active learning,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.645948Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:3bae554f1ba681079bca72ae0854d665da69e37c418b6b13f544f8f5c164b360","observation_id":"77a2626f-6358-4fb9-97b5-8ea9857195df","resolution":{"observed_at":"2026-08-15T23:47:40.645948Z","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":"2026-08-15T23:47:40.650971Z","title":"One thing one click: A self-training approach for weakly supervised 3D semantic segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.650971Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:b0ccc72ecaeaeebfd33ecde71a2b76a6190cd3e272d8064edd5e71c121292a59","observation_id":"51fd0023-c953-4a57-b375-a0ba22ff6afa","resolution":{"observed_at":"2026-08-15T23:47:40.650971Z","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":"2026-08-15T23:47:40.655849Z","title":"SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.655849Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:9ed01c1416720cc57abb7b1442cc8f97320018de6b8679d50f23402602737fee","observation_id":"7a88b3d7-68a3-4475-bd48-6be2662dcf1e","resolution":{"observed_at":"2026-08-15T23:47:40.655849Z","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":"2026-08-15T23:47:40.660429Z","title":"KPConv: Flexible and deformable convolution for point clouds,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.660429Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:604654f42910af5940f61b6bca5524ef463fae432a0b47de7d51a78e918c593c","observation_id":"bcac0a9c-b4f4-435c-81b8-990d65474c48","resolution":{"observed_at":"2026-08-15T23:47:40.660429Z","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":"2026-08-15T23:47:40.664778Z","title":"Unsupervised multi-task feature learning on point clouds,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.664778Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:3fde9a25387bf2eb51d56dbf27bdf767ff79511eaec0d35e0e71adadd05ab79c","observation_id":"baa7d8b4-f40f-42b9-b096-14d2975cb914","resolution":{"observed_at":"2026-08-15T23:47:40.664778Z","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":"2026-08-15T23:47:40.669625Z","title":"Self-supervised learning of local features in 3D point clouds,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.669625Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:4e8138ba6f861b4b669f01973d1f8cb096bc3da492fe3ef303ba8f639a1ca861","observation_id":"9155fc11-a2a7-49bf-a12a-df768b84d898","resolution":{"observed_at":"2026-08-15T23:47:40.669625Z","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":"2026-08-15T23:47:40.674017Z","title":"Unsupervised point cloud representation learning by clustering and neural rendering,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.674017Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:53d341913eb6f6a3b51c2020f32d48252d3414258a65de5c9d05d7cf106e5813","observation_id":"83cd3201-eb64-4138-a1d2-4443518aacca","resolution":{"observed_at":"2026-08-15T23:47:40.674017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.02691","last_updated":"2021-01-07T18:55:21Z","snapshot_observed_at":"2026-08-16T18:53:39.150406Z","submitted_at":"2021-01-07T18:55:21Z","title":"Self-Supervised Pretraining of 3D Features on any Point-Cloud","version":1},"cited_work":{"arxiv_id":"2101.02691","doi":null,"metadata_source":"pith","pith_arxiv_id":"2101.02691","snapshot_observed_at":"2026-08-15T23:47:41.332529Z","title":"Self-Supervised Pretraining of 3D Features on any Point-Cloud","venue":"cs.CV","work_id":"aa841b39-1dad-4e9e-bb2d-f539b27374c9","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.678392Z"},"links":{"cited_paper":"/paper/2101.02691","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:904209dd513e5f13913bb10b391a80163de89e7a4597df580bcb0187c440aba3","observation_id":"5aa78834-4579-4a05-ac31-6412f3545110","resolution":{"observed_at":"2026-08-15T23:47:41.337563Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:40.683177Z","title":"Fusion-then- distillation: Toward cross-modal positive distillation for domain adaptive 3D semantic segmentation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.683177Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:637e3a2287fb61b55672df80abbcda02a8386dd035bba2156c62897a188fadde","observation_id":"a3d3bd74-dc93-4aa0-bd81-0e5c774ae594","resolution":{"observed_at":"2026-08-15T23:47:40.683177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.15286","last_updated":"2025-01-07T10:34:12Z","snapshot_observed_at":"2026-08-17T10:25:33.396640Z","submitted_at":"2024-05-24T07:18:09Z","title":"3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous Driving","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.15286","snapshot_observed_at":"2026-08-15T23:47:40.687529Z","title":"3D unsupervised learning by distilling 2D open-vocabulary segmentation models for autonomous driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.687529Z"},"links":{"cited_paper":"/paper/2405.15286","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:7e91307eba4d7f2f09df99a1c77726ea1cff912ab77375b356e3135bc15dfecf","observation_id":"9efecbca-0a74-4f71-b833-06bc3001c743","resolution":{"observed_at":"2026-08-15T23:47:40.687529Z","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":"2026-08-15T23:47:40.691909Z","title":"4D spatio-temporal ConvNets: Minkowski convolutional neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.691909Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:d31d820bb9fb3559df9a41d546cf9306c861ebaa2377363e14f18c4c68ac7d5d","observation_id":"55d0ae7a-32b3-4499-8026-57dd47a505e1","resolution":{"observed_at":"2026-08-15T23:47:40.691909Z","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":"2026-08-15T23:47:40.696154Z","title":"Multi-class active learning for image classification,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.696154Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:1fd5b9fa5007ded9f7fab1a4bc637abe2ff46f23fcac5343dcbcce6e12e58e9b","observation_id":"833a0c77-2120-4623-9a21-9ae0748c9d99","resolution":{"observed_at":"2026-08-15T23:47:40.696154Z","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":"2026-08-15T23:47:40.700373Z","title":"Searching efficient 3D architectures with sparse point-voxel convolution,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.700373Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:4f53651700b9ffcf32dd97f96a888c14cc6fc27ec32dfdd90c2ef73bc86c8a39","observation_id":"76d6bd0d-2a16-4262-bbba-c54ddd953e5a","resolution":{"observed_at":"2026-08-15T23:47:40.700373Z","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":"2026-08-15T23:47:40.704561Z","title":"A dataset for semantic scene understanding of LiDAR sequences,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.704561Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:beb506ace2a38455d3a79afad30e2fea8565f437c34b47d1f96cc71873ca58b4","observation_id":"dc0dc5ca-235f-48eb-85e8-cbaccaf1a880","resolution":{"observed_at":"2026-08-15T23:47:40.704561Z","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":"2026-08-15T23:47:40.708666Z","title":"Gaussian Mixture Models,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.708666Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:f8e0d98d246f51d5c550d45356b8c1ba5ed62c573d2b2f651669b6d178f4cc7e","observation_id":"9a74be37-a173-4b60-80de-a9c4ba4bb141","resolution":{"observed_at":"2026-08-15T23:47:40.708666Z","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":"2026-08-15T23:47:40.712936Z","title":"Least squares quantization in PCM,","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.712936Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:f73602f15de50f69b8ec354e8b5270fc4ff071a812548da8c73d74107683456e","observation_id":"93d64431-685b-407f-bb49-6784ac789af7","resolution":{"observed_at":"2026-08-15T23:47:40.712936Z","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":"2026-08-15T23:47:40.716970Z","title":"k-means++: The advantages of careful seeding,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.716970Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:1136e8d42e9c652d163c8634c2557b99f980c15305cc32efc3233a025e2cf3c8","observation_id":"3a7d8c4c-06dd-4d6b-9def-634cf9ce4925","resolution":{"observed_at":"2026-08-15T23:47:40.716970Z","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":"2026-08-15T23:47:40.721232Z","title":"Class- imbalanced semi-supervised learning for large-scale point cloud semantic segmentation via decoupling optimization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.721232Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:7ec451d343ec6b0b72ab6d3db1f4d173c2c91e66668da9192a4c5775baa6588e","observation_id":"d8da43ee-5ec5-46c0-9a33-83f78cad376b","resolution":{"observed_at":"2026-08-15T23:47:40.721232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09147","last_updated":"2020-02-21T06:10:34Z","snapshot_observed_at":"2026-08-15T02:29:02.439062Z","submitted_at":"2020-02-21T06:10:34Z","title":"SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances","version":1},"cited_work":{"arxiv_id":"2002.09147","doi":null,"metadata_source":"pith","pith_arxiv_id":"2002.09147","snapshot_observed_at":"2026-08-15T23:47:41.298349Z","title":"SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances","venue":"cs.RO","work_id":"8a7b0ef2-8bc4-4eb8-b0bf-1db6c3cc11a9","year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.726395Z"},"links":{"cited_paper":"/paper/2002.09147","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:4813aad9a25faf56d970b37c86ed2a44fe0695deb5ddfdbfc0eefef43b9f2772","observation_id":"464a0a97-184d-4a9e-803d-6e3f1a228c81","resolution":{"observed_at":"2026-08-15T23:47:41.303081Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:40.730928Z","title":"Deep learning-based LiDAR point cloud semantic segmentation for robotics: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.730928Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:9c682e709b03120c56ce625a974fe3c6cd6ea94afc14ebf769cdf30ffd05e466","observation_id":"c09fd80b-6cd3-4b2e-9efc-78d745ef4f27","resolution":{"observed_at":"2026-08-15T23:47:40.730928Z","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":"2026-08-15T23:47:40.735103Z","title":"LiDAR-based urban scene understanding for smart city applications: A review,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.735103Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:f43efc18dd8627a011532a6248f1e341c9a8c52ba68bac2e18a8dbbf8f0be9c0","observation_id":"0c397551-26b4-47cf-8413-19bac8697875","resolution":{"observed_at":"2026-08-15T23:47:40.735103Z","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":"2026-08-15T23:47:40.739173Z","title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.739173Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:0f687db8986442b4c36a7e8865a4c3dec8a99b14e22306bc6a17850bb2965ce0","observation_id":"0049edee-7244-4635-83b7-938fabddf46a","resolution":{"observed_at":"2026-08-15T23:47:40.739173Z","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":"2026-08-15T23:47:40.743166Z","title":"Spatio-temporal self- supervised representation learning for 3D point clouds,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.743166Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:961a52aa62a1a28293182507824a92d529953b387f3738153538ccfdbea54d7b","observation_id":"c1602e82-6ac2-4b38-bf08-e3a263ab99cd","resolution":{"observed_at":"2026-08-15T23:47:40.743166Z","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":"2026-08-15T23:47:40.747337Z","title":"Batch mode active learning and its application to medical image classification,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.747337Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:d0be8e1739a78ca1047de5f8cdb30cb8b9541696608bd9c38f6bfb04f4e83177","observation_id":"eb4db8fe-edaf-430d-993e-7f41d1d5b884","resolution":{"observed_at":"2026-08-15T23:47:40.747337Z","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":"2026-08-15T23:47:40.751240Z","title":"Active learning using pre-clustering,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.751240Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:a0faa4fe01d0e11a99f3edf80376c4d7a0e7edb2b97c8e9b1a1dcc8f6b5d92d7","observation_id":"7b65bb13-313a-4097-8872-838d33799491","resolution":{"observed_at":"2026-08-15T23:47:40.751240Z","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":"2026-08-15T23:47:42.255914Z","title":"Active learning with clustering,","venue":null,"work_id":"abe6900f-ecd9-445f-9c1d-6c0e75cfcf6f","year":2011},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.755698Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:a5344e906ff8262d6af37bdc87fa799b44d0d5214fbb5318b27da2886547a40c","observation_id":"5498d27c-e770-49c0-b6c6-6b3d6b09f924","resolution":{"observed_at":"2026-08-15T23:47:42.260615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.242058Z","title":"Subspace prototype guidance for mitigating class imbalance in point cloud semantic segmentation,","venue":null,"work_id":"9919b9f3-a8b4-4c25-a908-7d2c26b8a8ef","year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.759788Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:6773a1ab6ec99f7267fb9785c2c60bd4ea3f92403d4c2246fccc6e3396c8eafc","observation_id":"c3cc21cf-0d2f-459d-93da-e5d01e6e5ae1","resolution":{"observed_at":"2026-08-15T23:47:42.246845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.227995Z","title":"BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation,","venue":null,"work_id":"56fa50a8-165e-4774-954c-4e617f2ee093","year":2015},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.763828Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:6baa6da16e3f6261378d7cd574bb84b57570bd4484c44d6a724ee33c9d6bb077","observation_id":"c33d2d41-8cc6-4daf-ae0d-87bd01bfa2ad","resolution":{"observed_at":"2026-08-15T23:47:42.232477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.06347","last_updated":"2019-07-15T06:47:58Z","snapshot_observed_at":"2026-08-14T17:37:36.631621Z","submitted_at":"2019-07-15T06:47:58Z","title":"Discriminative Active Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.06347","snapshot_observed_at":"2026-08-15T23:47:40.767944Z","title":"Discriminative active learning,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.767944Z"},"links":{"cited_paper":"/paper/1907.06347","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:c7d8d3d4ff301657d57d12cc75e73a32bd58848e70d19b60e8955ce0002e24c9","observation_id":"b5d5fc94-c0a7-4729-8071-bf31f153fab9","resolution":{"observed_at":"2026-08-15T23:47:40.767944Z","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":"2026-08-15T23:47:42.213790Z","title":"Active learning literature survey,","venue":null,"work_id":"ae021a0a-7438-48f7-9e7f-98b9b5d792bb","year":2009},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.772226Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:bb409cbf12d50bc105b33a13443ab4ff0cbfc36b285ac0d59e92a24fe9ebbe01","observation_id":"a748c670-711d-43b1-84a9-e2f623717647","resolution":{"observed_at":"2026-08-15T23:47:42.218443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.199290Z","title":"Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation,","venue":null,"work_id":"29bc185c-86a5-479d-8b99-827070a0099f","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.776283Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:9ad6eacea588043e793fb7a85d7a0896fad50be01a497f6b64e40aab5a001756","observation_id":"f9f42617-61be-48b9-bba1-c78e5e2f88ed","resolution":{"observed_at":"2026-08-15T23:47:42.204062Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.185392Z","title":"A survey of deep active learning,","venue":null,"work_id":"f775f77c-aca2-4a6a-bd71-ce8c2e5d1b2b","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.780230Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:68c915996e87dd0b89d2c992a39ebc51b232d969c46a4c738a804b72d40693aa","observation_id":"782c4640-42cc-458a-927e-ae206dcedf40","resolution":{"observed_at":"2026-08-15T23:47:42.189853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.171624Z","title":"3D spatial recognition without spatially labeled 3D,","venue":null,"work_id":"e0d9f90e-bc76-4cf2-ad55-7b33fef95618","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.784362Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:fd50ac4106c4955737b7886116db2c631c7b07c6627ce4f017da749ac490ca8d","observation_id":"62ab8a3c-fa0b-4171-94e3-bcc9a8d16307","resolution":{"observed_at":"2026-08-15T23:47:42.176110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.157613Z","title":"A sequential algorithm for training text classifiers: Corrigendum and additional data,","venue":null,"work_id":"c9890b36-4c85-4625-bb62-56bae1f3aadf","year":1995},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.788933Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:a8da1e8dbec166f8513481d4925bfaee83a6837a923be44f5821b084f1d69135","observation_id":"c497c581-71cb-43ac-8452-27ea9840bd84","resolution":{"observed_at":"2026-08-15T23:47:42.162145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.143814Z","title":"A new active labeling method for deep learning,","venue":null,"work_id":"6ad2bbe0-90c0-4c5d-a8c8-718a6d7bcf91","year":2014},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.793185Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:ca9a7f9c49be98b59b611c2f38afe3cce172bc2749d71a9fc8878eca2707b729","observation_id":"45e87ffe-eb19-432a-8f34-fb42735af263","resolution":{"observed_at":"2026-08-15T23:47:42.148452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.130026Z","title":"Margin-based active learning for structured output spaces,","venue":null,"work_id":"a8c33281-7832-48ea-ab38-f3001758f877","year":2006},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.797310Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:367f2f25c60dd66c21f8bf7e4d08962532fd83a3241ef829bb32f103e47488c4","observation_id":"3e418129-57e5-4281-a2b4-a23c97e56571","resolution":{"observed_at":"2026-08-15T23:47:42.134451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.116054Z","title":"LADA: Look-ahead data acquisition via augmentation for deep active learning,","venue":null,"work_id":"d82f01d9-0adf-4ae1-afcf-d5c0633ea89c","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.801480Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:f59a8c89f7cba7ddac1e935a5102c99c0217f56af15e9f14a3635c805386fe72","observation_id":"e9de5b41-fe0a-411f-a68e-52ac6f56981d","resolution":{"observed_at":"2026-08-15T23:47:42.120694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.102872Z","title":"Active learning by feature mixing,","venue":null,"work_id":"90493640-7762-4388-9ebf-6feb063cfb9f","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.805498Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:c740f447058444c521a4fabf0b22ce83b1314ebd25098d7203d84e9a03f3f500","observation_id":"2f6e8160-90a0-4375-85c9-e565f59f41d4","resolution":{"observed_at":"2026-08-15T23:47:42.107279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.088451Z","title":"Heterogeneous uncertainty sampling for supervised learning,","venue":null,"work_id":"91bf8754-2ca3-46ad-8852-c941b8dc1329","year":1994},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.809777Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:265f8f270fa5d2981092df818f133f02efcc18d69c71d9166a02065b993e8e45","observation_id":"9ca71a67-8a39-4c98-95e1-a7b01bc5b9c9","resolution":{"observed_at":"2026-08-15T23:47:42.093530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.074793Z","title":"SQN: Weakly- supervised semantic segmentation of large-scale 3D point clouds,","venue":null,"work_id":"895ea3d4-7898-4be8-bdf9-a763160a200f","year":2023},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.814201Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:2f2e16bbd6b8c2529f697be6f98144d84c58495f0017520ada89aceffda930f3","observation_id":"ca0b67bd-c768-486e-b52e-860c67215355","resolution":{"observed_at":"2026-08-15T23:47:42.079393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.060724Z","title":"Bayesian generative active deep learning,","venue":null,"work_id":"156c2fe4-ba51-4057-b1bf-1a16161ac6ca","year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.818387Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:c88291af19adc3604eca75cc1fce2bf1452ac044a2fb8993ab3b76eb896167c1","observation_id":"7fa35552-078d-4c61-b3f9-df14968d7cd7","resolution":{"observed_at":"2026-08-15T23:47:42.065480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.046504Z","title":"BaSAL: Size-balanced active learning for LiDAR semantic segmentation,","venue":null,"work_id":"27a55c5b-dfc9-4dd4-a95d-07da05977706","year":2023},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.831828Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:9da3d9137bd232d9ef10fd6993c7c7bc4b3c14df181f0b83c875b571643c9cdd","observation_id":"05823538-424e-4010-8a45-4d46f7a34280","resolution":{"observed_at":"2026-08-15T23:47:42.050961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10986","last_updated":"2024-04-17T01:44:51Z","snapshot_observed_at":"2026-08-16T14:00:17.869177Z","submitted_at":"2024-04-17T01:44:51Z","title":"Melnikov Method for Perturbed Completely Integrable Systems","version":1},"cited_work":{"arxiv_id":"2404.10986","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.10986","snapshot_observed_at":"2026-08-15T23:47:41.237807Z","title":"Melnikov Method for Perturbed Completely Integrable Systems","venue":"math.DS","work_id":"bb96f4bf-5265-4e41-9964-2e3a1638c335","year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.835951Z"},"links":{"cited_paper":"/paper/2404.10986","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:7a598ccf0b37f747e38c963ae2ede56afb7bd84831f7688244288857d3ae1188","observation_id":"d7f53286-32c8-44b9-9f57-b61fa5e9323b","resolution":{"observed_at":"2026-08-15T23:47:41.242637Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.032228Z","title":"Active learning for point cloud semantic segmentation via spatial-structural diversity reasoning,","venue":null,"work_id":"a227112c-d513-4112-a67a-cda8e57f2be0","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.840391Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:baf1e6f05afcd651c0416c6014236d66277ebbc894c261baffd40b6301ed3c5a","observation_id":"09b3acd2-30db-4b90-bc6e-905bb5fd66b2","resolution":{"observed_at":"2026-08-15T23:47:42.037118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.018891Z","title":"Box2Mask: Weakly supervised 3D semantic instance segmentation using bounding boxes,","venue":null,"work_id":"c33540c0-69f9-4c5e-9ee8-e00b8aec07bd","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.844666Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:d2f5f5d3d45afd98dcd01bdbe0d414e06b423b1fb2e7d186bf48aa763be73989","observation_id":"b0f17194-b7bc-46a4-8467-ee1faf968556","resolution":{"observed_at":"2026-08-15T23:47:42.023448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:42.004742Z","title":"REDAL: Region-based and diversity-aware active learning for point cloud semantic segmentation,","venue":null,"work_id":"5fad260b-bc2f-42e9-97f0-e753456e2ffa","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.848760Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:1e23382a1a962e78b9de6a72152a9d5c795a27b0d084cef0742197328b3fccc8","observation_id":"270ce453-1577-4741-80e0-3d5a8ca068d4","resolution":{"observed_at":"2026-08-15T23:47:42.009377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.991329Z","title":"Exploring active 3D object detection from a generalization perspective,","venue":null,"work_id":"50b5e722-2878-44b8-89a3-06b31b4cf1cd","year":2023},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.852923Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:ae6c6afe474767ec72f7ba4f5fdf436a5708f1a73df4746c6c628ffd32e5cb7d","observation_id":"fe8fabb8-7742-4cdd-8da9-5d43292a8d18","resolution":{"observed_at":"2026-08-15T23:47:41.995744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.977509Z","title":"VMNet: V oxel-mesh network for geodesic-aware 3D semantic segmentation,","venue":null,"work_id":"b94c8b11-b868-4975-9afc-9a2fe0bdd709","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.857650Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:9a695f17aa6c70c6d4475980e716efcef366eb9598b0d50c36df24742744fe73","observation_id":"133a2e5e-ded2-4a90-bbae-37edf97e9905","resolution":{"observed_at":"2026-08-15T23:47:41.982270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.963793Z","title":"JSENet: Joint semantic segmentation and edge detection network for 3D point clouds,","venue":null,"work_id":"d3b8a1a1-d402-4cf9-a452-0828154ad66c","year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.861760Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:4cb388f4cca41bbc99b2ce71d91961912ade9b636138dce214849ea00a2a22a7","observation_id":"d2a1fa21-1ef2-4262-a735-c77cfd15ed7a","resolution":{"observed_at":"2026-08-15T23:47:41.968234Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1704.03847","last_updated":"2017-04-12T17:12:57Z","snapshot_observed_at":"2026-08-14T21:07:00.231965Z","submitted_at":"2017-04-12T17:12:57Z","title":"Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.03847","snapshot_observed_at":"2026-08-15T23:47:40.865896Z","title":"Semantic3D.net: A new large-scale point cloud classifica- tion benchmark,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.865896Z"},"links":{"cited_paper":"/paper/1704.03847","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:942ba0afa45d727ba0c2beeb8be3f31a876c6ee4e1f0860d7f4d87689827f9e2","observation_id":"fb16004d-0b8a-4d3c-860d-2b66212aba51","resolution":{"observed_at":"2026-08-15T23:47:40.865896Z","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":"2026-08-15T23:47:41.950867Z","title":"Multiple-instance active learning,","venue":null,"work_id":"91b44b48-fd93-4a8f-ad99-335214bb38ac","year":2007},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.870374Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:104b6728d926457fd628c4ab56da71beeb41b8b966d7ea7944f983eb5c6f9335","observation_id":"e26bf560-fa7b-4519-b699-ac49223b8b4e","resolution":{"observed_at":"2026-08-15T23:47:41.955042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.936823Z","title":"Active learning with statistical models,","venue":null,"work_id":"89743164-1060-4879-b477-3688ae217abe","year":1996},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.874656Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:ae7d1a24b695c4185811ada2b10e8167bc773246481751589f401ac97f262d65","observation_id":"c52d7384-9756-427f-b53b-587095244d3e","resolution":{"observed_at":"2026-08-15T23:47:41.941327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.923207Z","title":"Efficient learning on point clouds with basis point sets,","venue":null,"work_id":"0d831e6d-6f44-4733-b484-8f95ce53acb2","year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.878801Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:8c5f00b5a82ffc9a71da84b18ec3048f126ab7dcca0eb9d1e1f672768fe4cc3d","observation_id":"05314828-f3d4-4dec-b987-4718e3b5420f","resolution":{"observed_at":"2026-08-15T23:47:41.927665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.909359Z","title":"Multi-path region mining for weakly supervised 3D semantic segmentation on point clouds,","venue":null,"work_id":"4e1f5b9a-09f5-4a1d-9484-53eea3c8b0f5","year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.882807Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:3551738dbd74b70da2bcabf073f1ab20500aae5cca87197cfad4465886fbe728","observation_id":"74ba5f0f-5112-4156-91bd-f5e4c7b84595","resolution":{"observed_at":"2026-08-15T23:47:41.913877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.895893Z","title":"Divergence measures based on the Shannon entropy,","venue":null,"work_id":"8dc607ee-58c9-4d52-9344-3c3dd784ccaa","year":1991},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.887090Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:138ec69010ff51f143cc9dac4dd91becc63f69cc21775781b55656c71717b869","observation_id":"44d070f9-853c-47c3-ad89-3db0cc9c2438","resolution":{"observed_at":"2026-08-15T23:47:41.900182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.882583Z","title":"GroupContrast: Semantic-aware self-supervised representation learning for 3D understanding,","venue":null,"work_id":"cc3fddcc-a2f8-4434-8115-c7c7d46f8676","year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.891377Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:d55b6c2405a3fb1465589355a54fd697da71401be2116815ed5377d7a6f3eddc","observation_id":"b9db14ba-79af-4380-81b0-2da182047a90","resolution":{"observed_at":"2026-08-15T23:47:41.886981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00489","last_updated":"2018-06-01T10:17:23Z","snapshot_observed_at":"2026-08-16T13:51:24.988660Z","submitted_at":"2017-08-01T19:50:53Z","title":"Active Learning for Convolutional Neural Networks: A Core-Set Approach","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00489","snapshot_observed_at":"2026-08-15T23:47:40.895485Z","title":"Active learning for convolutional neural networks: A core-set approach,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.895485Z"},"links":{"cited_paper":"/paper/1708.00489","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:16d3362440eef0bad9a3f66655da8872c2eb685c024c3335af667af686bec87d","observation_id":"620ed7d9-7a3a-43f0-a0b3-0900708e8b8b","resolution":{"observed_at":"2026-08-15T23:47:40.895485Z","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":"2026-08-15T23:47:41.869275Z","title":"DeepCore: A comprehensive library for coreset selection in deep learning,","venue":null,"work_id":"9f594c9d-0008-48fc-8c67-b14382be47aa","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.899652Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:ce0fb4850e8694c9e3a8db3c38b32952e8d3edd595187ce5deb30b52506493be","observation_id":"c10a8bd4-19c4-4bec-be0d-3509d18a13bc","resolution":{"observed_at":"2026-08-15T23:47:41.873552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.855146Z","title":"Active learning through density clustering,","venue":null,"work_id":"6c20288d-6513-4b1f-a514-83bb5bc765a4","year":2017},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.903796Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:032fc3f9d093cc1d7f3794186cccdf9dbcdc18d25fb1938dda14b21803d6004d","observation_id":"89b34007-7e51-4e4f-bfaf-ba3d8b31a46f","resolution":{"observed_at":"2026-08-15T23:47:41.859988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.03671","last_updated":"2020-02-24T02:14:51Z","snapshot_observed_at":"2026-08-14T16:18:32.540444Z","submitted_at":"2019-06-09T16:52:09Z","title":"Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.03671","snapshot_observed_at":"2026-08-15T23:47:40.907761Z","title":"Deep batch active learning by diverse, uncertain gradient lower bounds,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.907761Z"},"links":{"cited_paper":"/paper/1906.03671","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:19afdcfb0836537797419c4fa5bc9c060703a5a8cd96df546a41290cb3a47991","observation_id":"6c7ab9b5-019e-4935-99c5-cefe41085cd5","resolution":{"observed_at":"2026-08-15T23:47:40.907761Z","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":"2026-08-15T23:47:41.840374Z","title":"Localization-aware active learning for object detection,","venue":null,"work_id":"01f797a8-11d8-42d0-98a0-b5e8907c5f0b","year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.911828Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:2ac49ac2915e574dd2907079687ef18c236a7f465ee78d8440166e89452de3c3","observation_id":"d2660b2c-4603-42f2-8613-48a3250b8930","resolution":{"observed_at":"2026-08-15T23:47:41.845295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.825582Z","title":"Annotating object instances with a Polygon-RNN,","venue":null,"work_id":"7da36131-3c92-4eba-ad65-a8bc7921e644","year":2017},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.916050Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:66fd543659b529d59b2aa1e300dd3c646436534200fa6c6006ef2fc7c6b83bb0","observation_id":"0e772983-04f8-40c5-b11b-35ca49a62587","resolution":{"observed_at":"2026-08-15T23:47:41.830023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.811578Z","title":"Uncertainty in deep learning,","venue":null,"work_id":"94197578-4bab-48ae-8520-0d17975ec089","year":2016},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.920012Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:ecf4b475deb911a796d7a409aeef2fe48f80b18928e40f1721df7128a0322b82","observation_id":"d709ce9f-ba4d-4b00-bdba-4365044fe6f5","resolution":{"observed_at":"2026-08-15T23:47:41.816216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.796951Z","title":"Demystifying multi- faceted video summarization: Tradeoff between diversity, representation, coverage and importance,","venue":null,"work_id":"42c9e277-e31f-433a-af82-70dfcc49fc83","year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.924305Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:3f255d054b11bdb19f8aabd71500f0e31571e825cd1ca378c544a76c7c8bd359","observation_id":"7c817163-c598-4518-b8c6-d4546b6732f0","resolution":{"observed_at":"2026-08-15T23:47:41.801839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.782202Z","title":"An analysis of approximations for maximizing submodular set functions—I,","venue":null,"work_id":"a4b613bb-6102-41be-856b-6edb913737cd","year":1978},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.928440Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:1e1135b70fa7bc4b68b476d66b740f57c4a16403536f171964600f645d4d5c3f","observation_id":"185b2c05-5c73-4bed-ae74-10fb6ec5ab17","resolution":{"observed_at":"2026-08-15T23:47:41.786907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1112.5745","last_updated":"2011-12-24T17:53:19Z","snapshot_observed_at":"2026-08-17T04:17:54.065503Z","submitted_at":"2011-12-24T17:53:19Z","title":"Bayesian Active Learning for Classification and Preference Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1112.5745","snapshot_observed_at":"2026-08-15T23:47:40.932863Z","title":"Bayesian active learning for classification and preference learning,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.932863Z"},"links":{"cited_paper":"/paper/1112.5745","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:60285785a95b3670d83cba8c587d6f17dbf1790d3f95b2af8316fc9892d95496","observation_id":"6a804503-60aa-4748-ae88-71bdd3a1fcdf","resolution":{"observed_at":"2026-08-15T23:47:40.932863Z","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":"2026-08-15T23:47:41.767842Z","title":"Scalable active learning for object detection,","venue":null,"work_id":"a4f2b0d6-b49d-4e43-9279-77f35c186c71","year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.937044Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:b472a6178560c5362ed8599e6f5916fa70517634ca6fbd6f76c355e4732ba680","observation_id":"b73b427b-0ab0-4131-8e40-d5148347e22e","resolution":{"observed_at":"2026-08-15T23:47:41.772623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.00132","last_updated":"2022-10-04T08:10:44Z","snapshot_observed_at":"2026-08-16T17:24:32.683207Z","submitted_at":"2022-01-31T22:41:35Z","title":"Submodularity In Machine Learning and Artificial Intelligence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.00132","snapshot_observed_at":"2026-08-15T23:47:40.941197Z","title":"Submodularity in machine learning and artificial intelli- gence,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.941197Z"},"links":{"cited_paper":"/paper/2202.00132","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:c05c5267d3ac53e38fef604d44cbf78f6993ac246bc034c5a33aa07f931a078f","observation_id":"e79792ec-0ff8-4fe3-8f42-f2f94b2dfe8a","resolution":{"observed_at":"2026-08-15T23:47:40.941197Z","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":"2026-08-15T23:47:41.753087Z","title":"Kecor: Kernel coding rate maximization for active 3D object detection,","venue":null,"work_id":"c98fd9c6-2741-4b34-8e1e-14bf0686746d","year":2023},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.945566Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:fea604059ff1d8c63851050043fbe09997a01fe67f42e763e6f312b5472e6bc7","observation_id":"b3a3ac0e-7cbb-453e-92ae-f1843a35846c","resolution":{"observed_at":"2026-08-15T23:47:41.758176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.738781Z","title":"Prism: A rich class of parameterized submodular information measures for guided data subset selection,","venue":null,"work_id":"08ec3d8b-e72a-4ce0-818f-c8789268e0db","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.949616Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:7f0cc688882c2d9b78a5e1f0948576e0ef0c9e317e9665d5666e4d2a5a9dccff","observation_id":"a710d250-fa3d-4525-88d1-bd3e202687d8","resolution":{"observed_at":"2026-08-15T23:47:41.743345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.724286Z","title":"Inconsistency-based data-centric active open-set annotation,","venue":null,"work_id":"8e78fbfc-1ed9-4892-8bfc-a3ed8988af0f","year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.953912Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:d3d8677ac34e107f46299143b8d7ef7a4ec33f7081947bdaa2562fba40227db0","observation_id":"8e5616ca-0db7-4a81-9377-98e5f3011483","resolution":{"observed_at":"2026-08-15T23:47:41.728819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.709205Z","title":"Similar: Submod- ular information measures based active learning in realistic scenarios,","venue":null,"work_id":"223336f2-80fc-4e70-b409-2ec632dad63d","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.958266Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:db751f0697de868bcb0810240a8d8524f25698377748cb532e8eed3b87324e02","observation_id":"7cca63cc-fbef-4a0a-8992-0fce03ee2f9c","resolution":{"observed_at":"2026-08-15T23:47:41.713960Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.694425Z","title":"Talisman: Targeted active learning for object detection with rare classes and slices using submodular mutual information,","venue":null,"work_id":"3880ca22-f16b-462b-99ad-4c7bcd69d377","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.962613Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:58aec064363a6e4ce522d03c7c275e5228f4150805ed77f0ea94c8ca77338c2f","observation_id":"bb869d66-c9a6-4648-bc7a-0bedc77cf729","resolution":{"observed_at":"2026-08-15T23:47:41.699254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.680271Z","title":"Submodular subset selection for large-scale speech training data,","venue":null,"work_id":"0ce47a03-2375-4d10-80ce-0123c1531621","year":2014},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.966659Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:66cace7a28ecbafa253fa9befbfad14d44e003a6c683d002ea7458206adad85e","observation_id":"dcab2045-823a-4483-906c-7b7c205c8074","resolution":{"observed_at":"2026-08-15T23:47:41.684645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.665159Z","title":"Automata: Gradient based data subset selection for compute-efficient hyper-parameter tuning,","venue":null,"work_id":"7a3379a3-6e0c-4a1e-b7ee-b86297078d7c","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.970582Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:f63ac745aa1dcaae94e435dacdeb79aa48fcfebd5a086238f8b02187d6c3e00e","observation_id":"9d84e15b-5b2a-4d94-ba49-b4f57dca9a5e","resolution":{"observed_at":"2026-08-15T23:47:41.669708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.650146Z","title":"GCR: Gradient coreset based replay buffer selection for continual learning,","venue":null,"work_id":"fa8c330b-72e4-4c26-bc95-663f8c2b78c6","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.974558Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:c37ca9e282caa7613020d4551f59e559482c525cecaeadcf2a1baf76a1ae4dbe","observation_id":"5cea87b3-38ee-4d98-926b-6ad87a4fc039","resolution":{"observed_at":"2026-08-15T23:47:41.654755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.635068Z","title":"Deep similarity-based batch mode active learning with exploration- exploitation,","venue":null,"work_id":"289c9c64-7b58-442d-80f2-c9bded31bd08","year":2017},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.978793Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:a365d2d87e30e8a238e3d09d5463aeaa713f691960e678fb2f29a0d32cba16b0","observation_id":"1df03ff9-bfd5-400a-b517-f00d77944471","resolution":{"observed_at":"2026-08-15T23:47:41.640430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.07975","last_updated":"2019-06-19T08:45:59Z","snapshot_observed_at":"2026-08-14T16:13:26.253688Z","submitted_at":"2019-06-19T08:45:59Z","title":"Batch Active Learning Using Determinantal Point Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.07975","snapshot_observed_at":"2026-08-15T23:47:40.983005Z","title":"Batch active learning using determinantal point processes,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.983005Z"},"links":{"cited_paper":"/paper/1906.07975","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:0b345244870973f284516aa07a741b74f631576c5de3aaf1958006329e85327a","observation_id":"3466c12a-4ec5-4939-86e6-68b42335d954","resolution":{"observed_at":"2026-08-15T23:47:40.983005Z","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":"2026-08-15T23:47:41.620480Z","title":"A mathematical theory of communication,","venue":null,"work_id":"c01c692f-5588-490b-b81e-c978bfd39982","year":2001},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.987359Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:45364e1c9ab0a281bb4af02db364bbf78db2f9c69c8a037525621ced1682ef2f","observation_id":"a10a816a-5647-40df-9793-695d01aa3883","resolution":{"observed_at":"2026-08-15T23:47:41.624897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.605008Z","title":"SUN RGB-D: A RGB-D scene understanding benchmark suite,","venue":null,"work_id":"6f5b7da7-3d46-4e6a-8427-a3c47bed33e6","year":2015},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.991704Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:6338884a841ef5afe37befd97cb5ee0a2f12e4138b2259700ce592238b9624c1","observation_id":"e802536d-9388-40f3-96cd-06b1473436ab","resolution":{"observed_at":"2026-08-15T23:47:41.610973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.590094Z","title":"Annotator: A generic active learning baseline for LiDAR semantic segmentation,","venue":null,"work_id":"6eaf11ef-c7fa-4178-802c-7fdedc0b6ffa","year":2023},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.995801Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:f75bec11d9d4baeb4e70425f3c4d65ca9d6285da0bec10854b4f4d8294791b2a","observation_id":"16464119-06ca-4886-b75c-660126539fca","resolution":{"observed_at":"2026-08-15T23:47:41.594532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.576625Z","title":"Are we hungry for 3D LiDAR data for semantic segmentation? A survey of datasets and methods,","venue":null,"work_id":"053ee2d0-ed6c-436d-bf3a-fecfd1b465d4","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:40.999844Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:ebaf3e622c3bf9481f4d1f66d2223f732c5155e2a0b1aa2cbc17bacf5ddbbdf5","observation_id":"88e05ec8-714e-4d67-a89f-7e8ac47f2a09","resolution":{"observed_at":"2026-08-15T23:47:41.580956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.562271Z","title":"Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset,","venue":null,"work_id":"43583572-0d20-4c66-a645-50421d554b4e","year":2021},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.003990Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:21a1cbebaf482bc4d5af113c74ac084143097dee24d3aa5f2d426300cdbb2af3","observation_id":"518186f5-bbce-411e-a7a6-51f3266d121f","resolution":{"observed_at":"2026-08-15T23:47:41.566817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.547828Z","title":"SECOND: Sparsely embedded convolutional detection,","venue":null,"work_id":"be8cb8cc-015a-4c64-a095-c667df6a6333","year":2018},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.008095Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:42e171406d0695896597745543f174b2cf4fe39f0fd86fa6baae984344b78f5c","observation_id":"03676bd3-59ad-419f-9564-3a859d6eb22d","resolution":{"observed_at":"2026-08-15T23:47:41.552517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.533649Z","title":"LESS: Label-efficient semantic segmentation for LiDAR point clouds,","venue":null,"work_id":"490708d0-afd2-4016-b443-029344f3de3e","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.012198Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:f646d9b7ea6515c55edb1811209a5d2d8fe912e67ef24b27954ab52ead8aff41","observation_id":"581ff59c-8efe-4428-a119-5ddc9dd99c4e","resolution":{"observed_at":"2026-08-15T23:47:41.538193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.518855Z","title":"Multi-class active learning for image classification,","venue":null,"work_id":"426721da-29c2-4219-a3bb-7d49519bd769","year":2009},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.016086Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:131ccdb9c524505e0dacc287f104b44417ba4213e2625643be821298f6aedea0","observation_id":"3ace750f-ebaa-49a9-951f-3b73c2a53a19","resolution":{"observed_at":"2026-08-15T23:47:41.523854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.504292Z","title":"Cost-effective active learning for deep image classification,","venue":null,"work_id":"2299f60a-c243-4603-85ac-1fd646f8a9d1","year":2016},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.020089Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:0f348b7a48aa1b7b63870d64fe1cabf7fc5c4fb73958a5af803a3546d229ebd9","observation_id":"369196f4-bee8-4f96-8c4e-a6eb1af4b752","resolution":{"observed_at":"2026-08-15T23:47:41.509192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.489762Z","title":"The power of ensembles for active learning in image classification,","venue":null,"work_id":"2e4bb14b-4efd-44bc-8d17-e5dc14b60e52","year":2018},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.024330Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:a484ee5f52970ff97cb84236f2a458a281ef3f1db1926827c435199154cce36f","observation_id":"599dea93-e65c-43c8-b402-c2c66bc855b4","resolution":{"observed_at":"2026-08-15T23:47:41.494293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.475343Z","title":"A multi-granularity semi- supervised active learning for point cloud semantic segmentation,","venue":null,"work_id":"9d5f040e-6c05-4113-9069-1714f52dd969","year":2023},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.028226Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:5b1405d53fc1f1cbd4fb35aa56754a272b0fea22514999745d4200ea2e8804a2","observation_id":"0d1e5b66-08e9-4e19-b38a-b14956f61bd6","resolution":{"observed_at":"2026-08-15T23:47:41.479665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02442","last_updated":"2022-10-05T17:59:50Z","snapshot_observed_at":"2026-08-16T16:26:38.305043Z","submitted_at":"2022-10-05T17:59:50Z","title":"Making Your First Choice: To Address Cold Start Problem in Vision Active Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02442","snapshot_observed_at":"2026-08-15T23:47:41.032170Z","title":"Making your first choice: To address cold start problem in vision active learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.032170Z"},"links":{"cited_paper":"/paper/2210.02442","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:4402f0c22febe6d995e405d43be6e903d7b6a8f7920e48c2729e18e4bcf9b13b","observation_id":"7b75d125-608e-44f6-8f88-336cc7ba3165","resolution":{"observed_at":"2026-08-15T23:47:41.032170Z","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":"2026-08-15T23:47:41.462170Z","title":"Addressing the item cold-start problem by attribute-driven active learning,","venue":null,"work_id":"2c3fb612-5dfd-41b2-aed3-fa1ac26460ef","year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.036500Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:576b6b31ddac61f4b25e9a2899b1dfe333e81aa149d7c98201ec5f9f9dfbd10f","observation_id":"4d649038-adcd-4d95-8334-12a068772177","resolution":{"observed_at":"2026-08-15T23:47:41.466522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.448258Z","title":"Cold-start active learning with robust ordinal matrix factorization,","venue":null,"work_id":"2fef26c3-7dfb-4026-9a46-848aabb2cfd9","year":2014},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.040564Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:76a033f65e534e77fd3b1da1de1ccc68cbe172376aa170f0afb9bb2d6b36ccbd","observation_id":"6df9f872-f052-4354-9472-eb9bcde1b0f7","resolution":{"observed_at":"2026-08-15T23:47:41.453023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.434285Z","title":"Gaussian mixture models,","venue":null,"work_id":"05180f8f-6291-4947-9edf-73dff1c9b912","year":2009},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.044444Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:57d19095df5f96006d509f8fcfc0df8b4835817de0228fa13c1ee59081209153","observation_id":"05792d89-7443-4147-84b3-a0bbc5b6d3ad","resolution":{"observed_at":"2026-08-15T23:47:41.438368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.420074Z","title":"Active learning for point cloud semantic segmentation via spatial-structural diversity reasoning,","venue":null,"work_id":"37370c64-0987-4f40-b140-cd2f34a6dba1","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.048535Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:4879df1b4f4f921e6cb87e4a75da6bbc2deccd65d80eecfeb33ef19caed95b2a","observation_id":"4ca10605-61ea-4a5f-9883-2a55e0d4a86a","resolution":{"observed_at":"2026-08-15T23:47:41.424793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.403698Z","title":"LIDAL: Inter-frame uncertainty based active learning for 3D LiDAR semantic segmentation,","venue":null,"work_id":"f38f92fb-ffd4-46ac-baca-2982c97e55d6","year":2022},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.052483Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:39f5a77ffe8650655f68e6513ede54fb9899801234c07a3b01175c2935a30639","observation_id":"d8e759ba-cd14-4974-ab61-f12c0b782083","resolution":{"observed_at":"2026-08-15T23:47:41.409614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.389638Z","title":"Active learning for deep detection neural networks,","venue":null,"work_id":"dce92f36-8abb-40da-b502-d15eb24be80d","year":2019},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.056709Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:04c6f91c16e8f5aedd727a0397771431e7e3b6a7aa855d189325f202de9401fc","observation_id":"d4f9b01d-3cab-48b1-b7f1-9441daf00f77","resolution":{"observed_at":"2026-08-15T23:47:41.394091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.03918","last_updated":"2024-11-01T05:23:35Z","snapshot_observed_at":"2026-08-16T13:12:26.284960Z","submitted_at":"2024-10-04T20:45:33Z","title":"STONE: A Submodular Optimization Framework for Active 3D Object Detection","version":2},"cited_work":{"arxiv_id":"2410.03918","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.03918","snapshot_observed_at":"2026-08-15T23:47:41.145821Z","title":"STONE: A Submodular Optimization Framework for Active 3D Object Detection","venue":"cs.CV","work_id":"1f27bd57-3d4e-4aaf-be82-a1e49e9b4e74","year":2024},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.060704Z"},"links":{"cited_paper":"/paper/2410.03918","citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:52f9e81b535fd3c901631d780a8bf5defdbe8424f41cf0b5b819cec587730817","observation_id":"d803664b-ab90-4dc9-855f-da356a4e52d0","resolution":{"observed_at":"2026-08-15T23:47:41.152376Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.375998Z","title":"ViewAL: Active learning with viewpoint entropy for semantic segmentation,","venue":null,"work_id":"53111387-4be0-4c2e-adc3-a304f6a5a774","year":2020},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.065021Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:77187d893494cff45f8a62ded78aff5ced2a97bcb4d301ae579fc1308f0c27fc","observation_id":"6dd997f7-b394-4742-a455-3d03419458b8","resolution":{"observed_at":"2026-08-15T23:47:41.380739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T23:47:41.361088Z","title":"Suggestive annotation: A deep active learning framework for biomedical image segmentation,","venue":null,"work_id":"4336c71c-030a-4000-9b79-9dbbdc45ddfc","year":2017},"citing_paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-15T23:47:41.069280Z"},"links":{"citing_paper":"/paper/2505.11516"},"observation_digest":"sha256:b4722a56c1e159c78bd8c120cb528a36ecab3a519cd42e2661d1211787ca7c03","observation_id":"4faa8dab-2cb3-49c8-b960-dba09b34f542","resolution":{"observed_at":"2026-08-15T23:47:41.365795Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.11516","last_updated":"2025-05-06T20:50:53Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-15T23:38:48.288707Z","submitted_at":"2025-05-06T20:50:53Z","title":"SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":32,"verified_exact":2,"verified_fuzzy":64},"total_outbound_references":103},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 0 inbound Pith citation observations for arXiv:2505.11516."}