{"as_of":"2026-08-12T20:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8430794fc21c9b9fc8286a89970596fa26c68cf2736230da3a59b8664833bf81","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-15T19:27:52.512963Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2602.21484/citation-record","integrity":"/paper/2602.21484/integrity","json":"/paper/2602.21484/citation-record.json","paper":"/paper/2602.21484"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"V oxel r- cnn: Towards high performance voxel-based 3d object detection","venue":null,"work_id":"f55989a8-fab6-4fa5-9b00-121302ab2944","year":2021},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:c26c2e26af0676b84eb9e04e3c3c8656a35bdd72526b845c3d569ce39ee85398","observation_id":"3f37f007-7dec-4d92-89c1-36af4a3e916b","resolution":{"observed_at":"2026-05-15T19:30:18.462646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-09T09:16:07.046973Z","title":"Pv-rcnn: Point-voxel feature set abstraction for 3d object detection","venue":null,"work_id":"bdd48c9c-318b-4e0a-a491-854a1627933f","year":2020},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:ba019efb929e3d8c720cb48ffc8e97c8e10dd16d9898b00773b57affc43107e5","observation_id":"00102b8e-7965-4b6b-b021-17fb79b4650f","resolution":{"observed_at":"2026-05-15T19:30:18.387542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13542","last_updated":"2024-09-01T03:33:26Z","snapshot_observed_at":"2026-08-12T07:19:13.261854Z","submitted_at":"2022-05-26T17:59:35Z","title":"BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation","version":3},"cited_work":{"arxiv_id":"2205.13542","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.13542","snapshot_observed_at":"2026-07-03T21:08:58.812391Z","title":"Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation","venue":null,"work_id":"c01b4bfc-52e6-448b-b7e7-c5712c659a93","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"cited_paper":"/paper/2205.13542","citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:571ea541f8eeb3bb2e357f57b8b84aeaead0d6b7f31cf52744ec3b9a9b0b5444","observation_id":"8b644586-9363-4a86-a313-f8a6165e9c1f","resolution":{"observed_at":"2026-05-15T19:30:16.732787Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Cross modal transformer: Towards fast and robust 3d object detection","venue":null,"work_id":"c4d2d7f4-b176-4159-b46e-3bf4668eb924","year":2023},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:4c98ba48a6ff9179ec40e32c30a3f96064f77e21861546c0b8e48531e24bf23d","observation_id":"eae35d79-7450-4fc3-bb35-baefbba7af8e","resolution":{"observed_at":"2026-05-15T19:30:18.390977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","venue":null,"work_id":"dc427893-ecc0-47a3-8fe5-2581e4309891","year":2012},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:98d6cc10612dd21635eb91951f192808ad9f239d80da103fd84a10663d97947f","observation_id":"1a277c5d-856f-4794-9339-1758b136761b","resolution":{"observed_at":"2026-05-15T19:30:18.408003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"nuscenes: A multimodal dataset for autonomous driving","venue":null,"work_id":"751435cc-156d-4743-8e0c-095c1931afce","year":2020},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:94039db9cab2f322f27e0485dd3178f9c2c0102356a2a81735eda631ee04eac2","observation_id":"50d8662d-19c5-4305-9a81-d798beba5861","resolution":{"observed_at":"2026-05-15T19:30:18.383452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-09T05:46:01.855450Z","title":"Scalability in perception for autonomous driving: Waymo open dataset","venue":null,"work_id":"cf5ae3fc-bc37-46a5-87ce-fb9a52cded92","year":2020},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:175f4789b703d6762b9622d9b8959d0fcb229b29093fa8c57c4977826e8dbce3","observation_id":"b232d0ca-42aa-43e4-96c4-64f5fba4ca01","resolution":{"observed_at":"2026-05-15T19:30:18.428049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Towards unsupervised object detection from lidar point clouds","venue":null,"work_id":"da0cfcb4-6813-41a4-ac8d-0df28394c0b6","year":2023},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:4339148701ff0dfb9f7a6d959622b032e9f3aefca6cba68fa8d6a07abe68aa45","observation_id":"6471ac44-45b3-4b33-bdf9-ecf7659a09f2","resolution":{"observed_at":"2026-05-15T19:30:18.440142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Commonsense prototype for outdoor unsupervised 3d object detection","venue":null,"work_id":"0dea5c01-50d9-4ed9-88b7-9c1956c93255","year":2024},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:ad37f189cb95b7c33f02db75efe98cd153f82a8d4d5e90dca19516f1f948f8bc","observation_id":"8c2171ad-81da-4f51-a366-db3db3c73f79","resolution":{"observed_at":"2026-05-15T19:30:18.423016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Learning to detect mobile objects from lidar scans without labels","venue":null,"work_id":"66db4f0b-74cc-46fe-8c63-af42ec7306af","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:e7fb54cf40f5a0546965d3df76a55d814760004ddbe7411ab72ed3e1408f6546","observation_id":"cb717513-90c6-4dc7-9d36-0cef60c88921","resolution":{"observed_at":"2026-05-15T19:30:18.447940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Liso: Lidar-only self- supervised 3d object detection","venue":null,"work_id":"b9845c55-6d59-4b6a-990a-311d2b1f695c","year":2024},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:d242a742cdc310495b2d8583d33e04e5ba80703072682398c6877459d7c317f2","observation_id":"c38fc37b-dcba-4367-b0df-fc01500d7325","resolution":{"observed_at":"2026-05-15T19:30:18.455850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Motal: Unsupervised 3d object detection by modality and task-specific knowledge transfer","venue":null,"work_id":"91efb62c-eb25-4e8d-b6fd-d223ff43190e","year":2025},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:ab3021aa86488e75f6044f730e42e7d5878c389ba1d3e25442c94a83efe3d667","observation_id":"c7b4cbca-2d52-45f2-9e82-37a9f9667ca4","resolution":{"observed_at":"2026-05-15T19:30:18.470006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Fgr: Frustum-aware geometric reasoning for weakly supervised 3d vehicle detection","venue":null,"work_id":"0aaa0247-1802-4145-8af9-264169c73cc5","year":2021},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:0b2469fede72132b2025b08bbd45b0762c858ce0cbbf78a60915469e30d83726","observation_id":"3106ded2-8cc1-45d9-b324-c53a454eaf75","resolution":{"observed_at":"2026-05-15T19:30:18.379265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Annofreeod: Detecting all classes at low frame rates without human annotations","venue":null,"work_id":"9febae16-cc6f-4208-9d8b-0a847833c091","year":2025},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:10ad9bc9d9a533ff7fb189749018b5fc288f71ab858f117e28021aff81fb9d37","observation_id":"6d26f0ba-2a54-4595-9b02-e4507ddb396f","resolution":{"observed_at":"2026-05-15T19:30:18.423798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Enhancing pseudo-boxes via data- level lidar-camera fusion for unsupervised 3d object detection","venue":null,"work_id":"a37c853d-96df-41a4-b3d7-9d1e4dea4ca7","year":2025},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:66f65a95c324a24832301680b148c5b02fce889958626263b6c2438e81ec7f90","observation_id":"b6387d6a-069b-4852-bd60-a35f0d7fc7ef","resolution":{"observed_at":"2026-05-15T19:30:18.452142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Approaching outside: scaling unsupervised 3d object detection from 2d scene","venue":null,"work_id":"565b9c25-4986-4898-b0d6-163200fa21f9","year":2024},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:4500bb627b95c3152cde91a4a349940e0fe81a9075e488cca34a660f1c9a000f","observation_id":"eff764fb-6678-48c6-b1b8-85545c7e1815","resolution":{"observed_at":"2026-05-15T19:30:18.465981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Union: Unsupervised 3d ob- ject detection using object appearance-based pseudo-classes","venue":null,"work_id":"e1934789-6134-43b9-83e7-0fa0052d492a","year":2024},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:44a36bbc9aaf9e1d69d15864a2f1c246e3c4eef6769f5d29c0f95d2591486402","observation_id":"b9b21bb8-60ee-48d0-9b88-2aa2d34ac059","resolution":{"observed_at":"2026-05-15T19:30:18.431192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Coin: Contrastive instance feature mining for outdoor 3d object detection with very limited annotations","venue":null,"work_id":"657ac3a1-2dfc-47c5-958a-a6156becc8db","year":2023},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:f2dd436d593357cea37d3afab02a081f0b32d916a637b0ee420a64637d9f5ca2","observation_id":"faa9c3e0-72d6-42b9-bcd8-9053413551fc","resolution":{"observed_at":"2026-05-15T19:30:18.426891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Learning class prototypes for unified sparse-supervised 3d object detection","venue":null,"work_id":"844134c1-0019-4425-8d65-241ae1921065","year":2025},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:dcd71162ef610ed80cf3962bb54f87845b962fccb6787365249bcaeb479ff2f3","observation_id":"c6743666-d09a-4a7a-8aab-c64a1323058d","resolution":{"observed_at":"2026-05-15T19:30:18.443694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-09T09:16:06.992645Z","title":"V oxelnet: End-to-end learning for point cloud based 3d object detection","venue":null,"work_id":"05f6f95c-4a27-4d93-85e0-1ccfd2ea7eca","year":2018},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:891841fea03360639fdfe9984c5bcc9d294d7796f047642477b02d8d452ae779","observation_id":"34e2d249-8016-408f-a720-39108af5f1d7","resolution":{"observed_at":"2026-05-15T19:30:18.419247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-09T09:16:07.086193Z","title":"Second: Sparsely embedded convolutional detection","venue":null,"work_id":"8d6d290c-1728-4ff9-ae91-dbf33a30aa7f","year":2018},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:28a7a7ca8da333298d2e0959213f8d4d0a3cf53397dd072d6b98e3473edcd2e2","observation_id":"98e8d2ff-0bdc-4c7f-96df-ab21971de8a3","resolution":{"observed_at":"2026-05-15T19:30:18.436135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Center-based 3d object detection and tracking","venue":null,"work_id":"88c3ef67-29c9-49bd-bdf8-7e952fd7b023","year":2021},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:cecb900eab335077268c210ba030ba477e1b322feba2ffc5313bb42d5eaa676a","observation_id":"421f7b6d-21f0-4e2d-aa1f-6dd3e334a347","resolution":{"observed_at":"2026-05-15T19:30:18.459223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-09T09:16:06.986873Z","title":"Pointpillars: Fast encoders for object detection from point clouds","venue":null,"work_id":"dc1c9dec-4ce0-4e99-94c5-47caafb15325","year":2019},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:7bd74edab46ad70e2225521e5f2337aeda430a58353574ff26b6ebf2acefb141","observation_id":"f739696b-2a65-4571-a0f2-b37787be9cf3","resolution":{"observed_at":"2026-05-15T19:30:18.474256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Smoke: Single-stage monocular 3d object detection via keypoint estimation","venue":null,"work_id":"aa5838e6-4fda-4cab-afe3-8ca566c77ec5","year":2020},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:fc92e8666b40c2533845bbf3e180e0d1bc91324e1cc32e4c37b87a0efcb61eb3","observation_id":"2ef9a3fa-00f0-4a1a-935c-492fc2a5be65","resolution":{"observed_at":"2026-05-15T19:31:31.751731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Categorical depth distribution network for monocular 3d object detection","venue":null,"work_id":"c3ba5696-4c0d-4842-b808-1973f555dd75","year":2021},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:d9eca73569570ffc06929eb9e7ae3670e5f0c1007bed1c2aa003528a9e488463","observation_id":"b37208be-b6d3-4785-84a0-de740bf1058d","resolution":{"observed_at":"2026-05-15T19:31:31.734725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Petr: Position embedding trans- formation for multi-view 3d object detection","venue":null,"work_id":"633c7909-f492-4be1-9033-2bd8d7ae5ada","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:61fb87053e24f3a6cd0ca46f6d5d61df1cbb2296de45df4aa134598db805f8a6","observation_id":"8c148886-13ec-4ff5-b914-9997ab9c57af","resolution":{"observed_at":"2026-05-15T19:31:31.729917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Mvx-net: Multimodal voxelnet for 3d object detection","venue":null,"work_id":"576997cb-5216-4e4a-8de6-a22973db40fd","year":2019},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:10c4c5b312767973533c9ab61081129344ee996e00fc32f388700a236906ac2b","observation_id":"adc8ff13-1896-42b6-af74-b582ba2f2c13","resolution":{"observed_at":"2026-05-15T19:31:31.714558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Synet: A synergistic network for 3d object detection through geometric-semantic-based multi- interaction fusion","venue":null,"work_id":"725d2081-ef3e-4725-a089-cebe4eded153","year":2025},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:5861a30c79f705fc3e9c2d26ad2d621c32674cfcc11087bca5790f7cdaaf36f7","observation_id":"d16f8ad5-d720-4f92-ab3e-95dff4858cf3","resolution":{"observed_at":"2026-05-15T19:31:31.806669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Detection, classification and tracking of moving objects in a 3d environment","venue":null,"work_id":"4aa8b900-1ad9-4711-9992-f845b72867e9","year":2012},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:72fd3b702e2df6a1eaf1a503490c51d2886bee11e3b1696a3f614a309419ad0f","observation_id":"a422f19c-937a-4b8b-992a-7fc6ea542e0f","resolution":{"observed_at":"2026-05-15T19:31:31.739532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Robust moving objects detection in lidar data exploiting visual cues","venue":null,"work_id":"f0702e8e-bae0-4342-8439-1c8b023ecd67","year":2016},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:e4e0eeea8d4d275d785a426350dcbc438b1b766cd6152cea01cbf2687726c482","observation_id":"5ece3099-329a-4102-8163-75169e5b0acc","resolution":{"observed_at":"2026-05-15T19:31:31.793094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Dynamic multi-lidar based multiple object detection and tracking","venue":null,"work_id":"9f06c3fb-7bb4-4f84-a3ee-ba773bf4b790","year":2019},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:12d6963714ac8fe63be3db6ce8c03d077eac272cfb86a818bf99517742b2d251","observation_id":"bb2af791-4740-4f4b-8792-7751d65b88e5","resolution":{"observed_at":"2026-05-15T19:31:31.719667Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Ss3d: Sparsely- supervised 3d object detection from point cloud","venue":null,"work_id":"e814eaf0-003f-42a2-9e47-13af2a8d4dbd","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:0930d6ae2e98ced9bba38a034b3e94175b5d3c2d9987a1e4929bb2c2dc2ca2ec","observation_id":"ffd90867-e1bb-46c3-9d4c-651a81ce1536","resolution":{"observed_at":"2026-05-15T19:31:31.765514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Hinted: Hard instance enhanced detector with mixed-density feature fusion for sparsely-supervised 3d object detection","venue":null,"work_id":"ebc443dc-155b-4c26-b70b-8345e7ff09e6","year":2024},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:fdd943e910bffdb704161e576f29b06754619384d9007f83cf507051dbd8ec85","observation_id":"e6668e32-55a3-4222-92dc-619c949cfd6c","resolution":{"observed_at":"2026-05-15T19:31:31.825544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Sp3d: Boosting sparsely-supervised 3d object detection via accurate cross-modal semantic prompts","venue":null,"work_id":"6031dd14-07ad-433c-8d4f-40246668ad94","year":2025},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:dfe43e5b1ae6ea824429c0fc5677d165a810c01d3e31dab8f64a583c35d91371","observation_id":"e495abe2-fcc7-4c04-9838-ea0db06a1039","resolution":{"observed_at":"2026-05-15T19:31:31.813129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Hardness-aware scene syn- thesis for semi-supervised 3d object detection","venue":null,"work_id":"20101d98-5680-4fbe-9fb3-7c7540e3a286","year":2024},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:744926a6a98f580c6e9d19932c82903c84ccaaca688ca1621630460965deb484","observation_id":"0c14bcb8-8894-4659-80ad-01ebf33a8eb9","resolution":{"observed_at":"2026-05-15T19:31:31.775598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Weakly supervised object detection with class prototypical network","venue":null,"work_id":"6ce31d2f-8b79-4bbc-b303-f32b38b49880","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:1d816b994a5ef7bcdc88eeabcaf6458643ddc0db72ad350c03b68b739b62fe1e","observation_id":"9cf839e4-f948-4e7a-beb8-0684d84cd5a4","resolution":{"observed_at":"2026-05-15T19:31:31.819815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Break- ing immutable: Information-coupled prototype elaboration for few-shot object detection","venue":null,"work_id":"de88f3c7-9d2e-454b-91bf-e7f1223c2275","year":2023},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:1b2a1f1853519719025be47c91af803fd9a4f667289a268985b5996f1a9b1f61","observation_id":"406e137c-11df-4ec6-8a04-0d8c6fad7437","resolution":{"observed_at":"2026-05-15T19:31:31.831413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Query- guided prototype evolution network for few-shot segmentation","venue":null,"work_id":"0cb13585-6412-4cee-9e39-d3ab1501773f","year":2024},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:0df07ba5225b40ec874be0035a87b315d1e1cc4aa7ce2f1ee149ac631efb7791","observation_id":"5db8efb5-0324-41b2-889f-786adbc6142e","resolution":{"observed_at":"2026-05-15T19:31:31.845092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Prototypical votenet for few-shot 3d point cloud object detection","venue":null,"work_id":"b20e1081-f4dd-4321-b9f3-ad425f36d013","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:394f7fc611bad85726af47d071d0bfdf5b4d5271961a8939a7a90fd559f4afd6","observation_id":"2af1a440-8027-4ab9-89ce-b948ed2d3e4e","resolution":{"observed_at":"2026-05-15T19:31:31.839135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Gpa-3d: Geometry- aware prototype alignment for unsupervised domain adaptive 3d object detection from point clouds","venue":null,"work_id":"7234f6eb-b0b1-48aa-9828-47f2957df7d2","year":2023},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:bd14cc7c2025a10d96655de83c83bd1e57814614f74d75dd8a3a0416219ce58f","observation_id":"e69c933e-04fe-4130-ae2b-f530d1e73ba7","resolution":{"observed_at":"2026-05-15T19:31:31.857931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Cl3d: Unsupervised domain adaptation for cross-lidar 3d detection","venue":null,"work_id":"87cadd46-502c-4cb3-bc19-c9b05cad43cd","year":2023},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:660cafaf65922746fdae8b51a78ce08f3fcdab21a4bec08f6431d641e6386148","observation_id":"be5f68d3-bdba-40fd-bb6c-6d856d857d96","resolution":{"observed_at":"2026-05-15T19:31:31.745701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Momentum contrast for unsupervised visual representation learning","venue":null,"work_id":"5cc1bfd4-208a-4a40-a9e0-fe6c6e6f9d6c","year":2020},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:874161f96ca8068c8d06ae70f8b15de5b3caea3577c9697d37dc142613d37f01","observation_id":"aeb3cc61-dc01-4a35-87e1-32f159adf34d","resolution":{"observed_at":"2026-05-15T19:31:31.787218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-08T10:14:52.411567Z","title":"Patchwork++: Fast and robust ground segmentation solving partial under-segmentation using 3d point cloud","venue":null,"work_id":"963001ce-9dc5-45c4-bfbe-28634e5fef11","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:d06d780cd06abaf005d13def2149ffb2b337ebb980d2c4993f70e181be9da92a","observation_id":"a6f2fc35-66c4-46f3-8783-279dfaa9328b","resolution":{"observed_at":"2026-05-15T19:31:31.709875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12524","last_updated":"2025-02-18T04:20:14Z","snapshot_observed_at":"2026-07-06T20:38:20.545914Z","submitted_at":"2025-02-18T04:20:14Z","title":"YOLOv12: Attention-Centric Real-Time Object Detectors","version":1},"cited_work":{"arxiv_id":"2502.12524","doi":"10.48550/arxiv.2502.12524","metadata_source":"pith","pith_arxiv_id":"2502.12524","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"YOLOv12: Attention-Centric Real-Time Object Detectors","venue":"cs.CV","work_id":"b2da2ba7-83b7-4573-96e5-d6f2b8ef3897","year":2025},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"cited_paper":"/paper/2502.12524","citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:a3eee4511c4cdae42f7ca6e6700e9133966f8786140ad768196424878f860efe","observation_id":"4ef6b7b8-c0ee-4e79-9a6c-36a1e7bfd6ca","resolution":{"observed_at":"2026-05-15T19:30:16.743623Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.14651","last_updated":"2022-07-07T15:36:49Z","snapshot_observed_at":"2026-08-10T18:07:46.585398Z","submitted_at":"2022-06-29T13:45:03Z","title":"BoT-SORT: Robust Associations Multi-Pedestrian Tracking","version":2},"cited_work":{"arxiv_id":"2206.14651","doi":"10.48550/arxiv.2206.14651","metadata_source":"pith","pith_arxiv_id":"2206.14651","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"BoT-SORT: Robust Associations Multi-Pedestrian Tracking","venue":"cs.CV","work_id":"450934b5-2ed0-4d79-9dc2-cf3d20caaf84","year":2022},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"cited_paper":"/paper/2206.14651","citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:e30e173b83e45eb42e7285dae199322a4e323af021e4913dc5f58437783d2c74","observation_id":"2187c3ef-23af-49fa-8664-17c5936fb092","resolution":{"observed_at":"2026-05-15T19:30:16.745238Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-07-06T23:53:06.881095Z","title":"A density-based algorithm for discovering clusters in large spatial databases with noise","venue":null,"work_id":"80eeb944-2243-4ecf-8d0a-83ee6e52dbd4","year":1996},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:c5653257b3a20a595415dd33f40978fa72d4814c8c58d2f4eb3f263f56eb8111","observation_id":"fd6b362c-4887-47fd-add4-63959339be1f","resolution":{"observed_at":"2026-05-15T19:31:31.798416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"K-nearest neighbor","venue":null,"work_id":"315f6f86-d286-4fb9-b540-cbd41fb4e365","year":2009},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:eca195e73f12e409bf08091c150568ac671b7be38a7aca521392346da4dc434f","observation_id":"2577072e-b434-4ab7-91b9-eebe5885f26e","resolution":{"observed_at":"2026-05-15T19:31:31.852482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"Efficient l-shape fitting for vehicle detection using laser scanners","venue":null,"work_id":"9638960c-86b3-4df4-9189-b80ecef4f9cf","year":2017},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:69827b662b4229b5a5a236b3f0d6cdcaa1a5baaf69a51592e693e40582bd6030","observation_id":"2b9bfdc1-809c-4b6b-b1db-a3798f5702ac","resolution":{"observed_at":"2026-05-15T19:31:31.724258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"V oxelnext: Fully sparse voxelnet for 3d object detection and tracking","venue":null,"work_id":"a41d308c-2d0e-4ee4-8c66-e0f2556413cb","year":2023},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:d06110aceab7275ade031a52fad9dc1b7f8fb525b2e211fe7047d361dec9c9ca","observation_id":"b7aeedef-56a0-4947-9ac5-d2c8545c61fb","resolution":{"observed_at":"2026-05-15T19:31:31.759136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":"These settings are selected according to the characteristics of KITTI and nuScenes to balance pseudo-label precision and recall","venue":null,"work_id":"b4cd763e-9bc6-40b8-8435-f6f222c632ae","year":null},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:0c94d2ee807a78205669245c1f1e63f8f359524736cda42768c0e23eb1bc3b9a","observation_id":"77ebe0b9-2330-43e3-bf1a-6cc157fe0d2f","resolution":{"observed_at":"2026-05-15T19:30:18.437133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e8990d6c-bf2a-4a80-a65b-79dcf391bd3e","year":null},"citing_paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:52.512963Z"},"links":{"citing_paper":"/paper/2602.21484"},"observation_digest":"sha256:63da9ed95c9b842e8401eb4f60cb02d14290f2f35f6f29703322e3149f610416","observation_id":"4c56aff8-5d6a-4386-9294-5e3c5c170b02","resolution":{"observed_at":"2026-05-15T19:30:18.432311Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2602.21484","last_updated":"2026-04-12T08:42:43Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T12:20:59.006624Z","submitted_at":"2026-02-25T01:26:34Z","title":"Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learning"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":3,"verified_fuzzy":47},"total_outbound_references":51},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2602.21484."}