{"as_of":"2026-08-09T05:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a3f83d480edb9f8542eb5dc77cb45b9f6b44355adc02498257d7603ae00f5d44","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T22:35:40.495590Z","state":"measured"},{"denominator":63,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":63,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2605.31177/citation-record","integrity":"/paper/2605.31177/integrity","json":"/paper/2605.31177/citation-record.json","paper":"/paper/2605.31177"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T22:35:40.495590Z","title":"RangeViT: Towards Vision Transformers for 3D Se- mantic Segmentation in Autonomous Driving","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:54403ece0f53b983a22b74585e2fc99e58ad36ee9d4913aaeb2665246cff8b5e","observation_id":"5d9e3acb-4f01-4853-957a-935a527c6c20","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Vivit: A video vision transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:3abf180ef69de84e358f296ebf2887029df5f33692ff5aab97fa6cedca165aee","observation_id":"342e6dba-ba0c-4032-99fa-4438c6d53a2b","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Behley, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:03df58f21cf0a48905f193675d02abcbdbf2d46ba8bdecd1725c7b5c2d7cbef4","observation_id":"359e7da7-8a82-400b-95d7-9a6a4b355e03","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Fkaconv: Feature-kernel alignment for point cloud convolution","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:32181fd6a94b18b997f2af4ae0ec7041938f2f0060ea9aa0b5d4c6cb7c92e002","observation_id":"a82a180f-dd33-4881-b0b6-29b0330e2e50","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krish- nan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:b1db60353e79a84752b68b6ac511e94a5d2133cf24de33e5db7f14b27b86b42d","observation_id":"426949bf-b63a-4038-8aa4-c3e745892f24","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Emerging properties in self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:9d0d8072a30aa159959de280389552cdfafe19415554a48b1e55910b65f60aab","observation_id":"d643f13d-db8c-4be9-befc-190c981e6d79","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"(AF)2-S3Net: Attentive Feature Fusion With Adaptive Feature Selection for Sparse Se- mantic Segmentation Network","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:30c00b5d54b60c2cc34bee84f45befbd8ae7f5f48a0d4a11f003dd0594b4a3e6","observation_id":"ba9663ca-201d-4e20-9965-25f10baa4c05","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"PointMixer: MLP- Mixer for Point Cloud Understanding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:cfbcb91a2be954c726ca188bd9ebe33bdb9fe04617d67c84dd88b5bec249584f","observation_id":"ce904e4d-2b73-4a17-9326-309b858d8d78","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:83f8eb8a733f28906f7f532db3df2ddbf342c480878fbd7cd4eee64528bc9f43","observation_id":"279f524b-57bb-49a2-8484-a6dc0688f988","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:877c3857a424d7af3af98f70eb7463feb4f98274ae54a08988667447dcd2dba4","observation_id":"3830651c-5603-467a-8a26-4f017de0620b","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Scaling vision trans- formers to 22 billion parameters","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:05fdbd69769c6f7ecf18978946c3de9d94078b26f661801df463e87254f8a08b","observation_id":"f1d499ff-42af-4323-9498-5154b4459391","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:9d74de9bcf2a6006ccc525c708bcf13de6de297cba555dae2f585503b2c8d8ee","observation_id":"c144bea9-9428-4153-bef7-41576fd399a6","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond in- ceptiOn module","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:10d1dacc0a67ba1815116c85347d55c662cdefbd61dd84fa4c50b558da971af6","observation_id":"6e0a711a-d882-4bcf-9851-b7687a4c1dbc","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.01778","last_updated":"2021-07-08T20:16:28Z","snapshot_observed_at":"2026-08-08T11:33:21.735489Z","submitted_at":"2021-04-05T05:26:29Z","title":"AST: Audio Spectrogram Transformer","version":3},"cited_work":{"arxiv_id":"2104.01778","doi":"10.48550/arxiv.2104.01778","metadata_source":"pith","pith_arxiv_id":"2104.01778","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ast: Audio spectrogram transformer","venue":"cs.SD","work_id":"b697ee73-6e22-4cba-84d1-4a1dab594872","year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"cited_paper":"/paper/2104.01778","citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:1654c10e87fbccbf644de86e77b6bc3e9ac30818819e8a67cbf74a3cd067fd10","observation_id":"1f230ac2-e242-4201-aaec-2785f9371ea4","resolution":{"observed_at":"2026-06-28T22:42:47.102000Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-07-13T18:20:56.483183+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T18:20:56.483183+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-06-28T22:35:40.495590Z","title":"Rotary position embedding for vision trans- former","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:773a0f9b1bfa54e92adccfc8ce37f36d8cdfacff7920f76df55e20b30cca9ae6","observation_id":"eb53c3cd-2da0-45d3-a798-d0c29275a4b9","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Point-to-V oxel Knowledge Dis- tillation for LiDAR Semantic Segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:e6fce3a11b174bf1c42657dc8db1876336c2ef4f6e1bb6449daa70a849346a47","observation_id":"eaa0458c-6b37-43ac-8fe9-2287d913e300","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Deep networks with stochastic depth","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:d2aba961d218efe7ea49f72326a0f1de70fcb4aad1d95712a5a151438d598d9a","observation_id":"b1571385-9e9e-4ab2-a2f3-bdbe54b9172f","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Dino in the room: Leveraging 2d founda- tion models for 3d segmentation.CVPRW, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:2a2e77746950c282519a17d1596ea629ba794b83ea4e690de384428ab7448102","observation_id":"84bb0dbd-2ea7-4609-8334-a8898246957a","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.12668","last_updated":"2020-08-21T10:43:18Z","snapshot_observed_at":"2026-07-06T09:41:38.009989Z","submitted_at":"2020-07-24T17:35:14Z","title":"KPRNet: Improving projection-based LiDAR semantic segmentation","version":2},"cited_work":{"arxiv_id":"2007.12668","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.12668","snapshot_observed_at":"2026-06-28T22:42:47.095464Z","title":"KPRNet: Improving projection-based LiDAR semantic segmentation.arXiv:2007.12668,","venue":null,"work_id":"0e9e753d-5d88-4dca-a623-61721f19b27e","year":2007},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"cited_paper":"/paper/2007.12668","citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:b76e75d149349df64280c80f689e3e5199b9e990d8bded1368affdd4a86c23a3","observation_id":"34084ba3-92fb-4d38-9bd0-89607282ad97","resolution":{"observed_at":"2026-06-28T22:42:47.096889Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-06-28T22:35:40.495590Z","title":"Rethinking range view representation for lidar segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:7ada691e2f2ef52e720b710b124c388d056a825b330fd5b756c6ef6140f4f579","observation_id":"e5e6f95d-770a-45b7-a046-cf4522b78d79","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Lasermix for semi-supervised lidar semantic seg- mentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:125cef3b48c6538fb94f5c99fd2d351c0f14f29ad5a5df1b2d109db055aa934b","observation_id":"9cc7d5f7-8391-4977-a509-4ef897f934ca","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Stratified Transformer for 3D Point Cloud Segmenta- tion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:1918b0c8fc7fc31594fea94175aa97e86d45a7568acea689ca9e134ed4cda415","observation_id":"4b81d156-6534-453b-bafb-d6c7c94c570d","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Spherical Transformer for LiDAR-Based 3D Recognition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:2d256b6dd928d116c4bac84cce0fc6a5d0b77977fc57909d4fe41f3148bf4714","observation_id":"317e36da-474f-45c2-9f65-c4b75267e4a9","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Large-scale point cloud semantic segmentation with superpoint graphs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:81a851715594bcc6ebec1e10f401777f037002ad5453872e3469656cbc3a15b4","observation_id":"03a612a9-f695-490d-aab6-3508d5712d31","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Self-distillation for robust lidar semantic segmentation in autonomous driving","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:7932f38bacc0f5e317ae449cc380cd45dccf9ad1882d3b8f44c19e1d8b5114e3","observation_id":"0afe1da4-e7aa-4be8-b77d-acb3c8d6d14f","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.04934","last_updated":"2020-12-09T09:34:25Z","snapshot_observed_at":"2026-07-06T10:22:05.629102Z","submitted_at":"2020-12-09T09:34:25Z","title":"AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation","version":1},"cited_work":{"arxiv_id":"2012.04934","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2012.04934","snapshot_observed_at":"2026-06-28T22:42:47.097947Z","title":"AMVNet: Assertion-based multi-view fusion network for LiDAR semantic segmentation","venue":null,"work_id":"2267786e-7ab4-4157-a8b5-0608c265eb67","year":2012},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"cited_paper":"/paper/2012.04934","citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:ec5b2b68c025bc1106c72ffe1a33d470992f6cf4414cea3031eaa4481478e942","observation_id":"e0c38081-ebc7-43a9-90b2-2bf8f7ea3d2f","resolution":{"observed_at":"2026-06-28T22:42:47.099464Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-06-28T22:35:40.495590Z","title":"Flatformer: Flattened window atten- tion for efficient point cloud transformer","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:d3c8609ad3d9d168d6b7c8aad71da11d3d101bb2f3ba3744c96d4142966f02db","observation_id":"bb53ee2b-bc48-44c3-90c6-aaf9c5dcc2ec","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:0499885b3e9f53157dc0d745ae2d4900b85bcee5813f23d05948ed895b7fbc87","observation_id":"7b055b47-ff6a-4eb4-8530-88c1957a6bfb","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:22a1361848e2bb84c9b8abd090fe6a01cac73e45b0604e3ba42b834db1d370c1","observation_id":"fc1a080d-9c29-4dfb-8b84-ace25829b665","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"RangeNet ++: Fast and Accurate Li- DAR Semantic Segmentation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:5e2c4bd1665601776a46bb7d487d3220f8d1df343c95e68736285c283e6b6cd7","observation_id":"5b7ed15c-b93f-419d-b067-eea0797a546f","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Fast Point Transformer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:99e7bcaf386498e6e71988e95962d094083000839bc088e2ffb6f033cd6e8581","observation_id":"d09a8ba2-ce7f-426d-af59-b3ce61cc098d","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution network.Expert Systems with Applications, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:fc34678b0d1cf0bd2d211883fa1618be28b5efc7e93e81c3e162331429ab310b","observation_id":"67941d85-95f2-4800-a3e8-ce91942a7c0b","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Using a waffle iron for automotive point cloud seman- tic segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:e3bea6215b753827ca26ebc259e950f6752ff74a78cf7b1564f99412636c3ba6","observation_id":"84e6d91b-4f56-43e2-9b84-6278820da762","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Three pillars improving vi- sion foundation model distillation for lidar","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:bfde25827d47eaebaf808713118d78a3e19d7fef78015aef9f43422ed816a63d","observation_id":"58d23dc1-7f44-400c-ab5d-e8b33bd8bb11","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Qi, Hao Su, Kaichun Mo, and Leonidas J","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:35206ddb1facba906ffa51778e661cd3113208397a3a5ca3114244b6cf5cd873","observation_id":"43829703-bd1c-409d-b4a2-7017dc625929","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:ffa82cc2b1394041291fdc8ae388e1541e8bcbab7c2d46491a1342549e282918","observation_id":"fb81292a-bf19-466c-8fc8-c7e7e131e302","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"PointNeXt: Revisit- ing PointNet++ with Improved Training and Scaling Strategies","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:1a8877af80433521cc8a78956da8cdfa82c9db7a766e4cee0dae78524d7bde6b","observation_id":"9042993f-b962-4f89-9657-bcd09fc97a7d","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"GFNet: Geometric Flow Network for 3D Point Cloud Seman- tic Segmentation.TMLR, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:1ca549bde395ecad3533de0a21bf1b08b4b1529308bb3871e986159bb996fbe4","observation_id":"e31e654b-1aa3-4d05-bb17-a11e72874bc5","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Rist, David Schmidt, Markus Enzweiler, and Dariu M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:de32c993b9d88b9bbeecc11c072c5bcd4683a6827be4454649b6e54e7eef35b4","observation_id":"e7bc6461-9573-4a00-a8d5-783f141fe7ef","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Efficient 3d semantic segmentation with superpoint transformer","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:3da94589953c40a034bfbaa1e0d187c30f79722cecf0b0951573fae1d477038a","observation_id":"b9c02527-d558-49e1-b1db-97dc7802527f","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"LMSCNet: Lightweight Multiscale 3D Se- mantic Completion","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:69e5b88ba1b6a86e48a04b097cd702a46e5b22355450fe884ee6f533f73ede02","observation_id":"1e00822f-d0ff-4c4d-946e-2f7fb919cce8","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Roformer: Enhanced transformer with rotary position embedding.Neuro- computing, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:dbc80293cad45e85bf14186525c91f5a0d547c83b6b824c6ea4f133c4cd01019","observation_id":"b8b4627b-4fca-416d-861c-c88ac2f5e217","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Scalability in perception for au- tonomous driving: Waymo open dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:2abc5edbc9fdc736e78163c5595412cb3b175dd522707f05fdca204355173da8","observation_id":"6030a574-df71-4347-a4c1-3cf2769f47f7","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Searching effi- cient 3d architectures with sparse point-voxel convo- lution","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:82251a7312eb73e7f73f8ad96d50d9624170e9cfa921a4ff3954e3b69523f191","observation_id":"c7f87a89-1fda-4519-a6b3-4c653d5966a1","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Qi, Jean-Emmanuel De- schaud, Beatriz Marcotegui, Francois Goulette, and Leonidas J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:d7a849fd9bfd15c044b06cd63310c213b74802ed72c85251ed1d53f5f83d99f5","observation_id":"a83cce55-ed51-442d-a883-bc340061dee9","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"MLP-Mixer: An all-MLP Architecture for Vision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:c37067ac3939616b383be13659cc5f57fa85858cb78098a376b7bacea2f3104f","observation_id":"c5be9ec4-b0f3-4b3a-9609-def8fe76a0d4","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Attention is all you need","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:67fe9b4534e19b0994d91f0a6259aa5bbf5c72ffd36faba9b5cd138c8234316e","observation_id":"3f452be7-84f0-4ba7-ac56-7a4f9a46fe40","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Dy- namic graph cnn for learning on point clouds.ACM Transactions On Graphics, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:9ae288ff610cedbbc121170144a98a0758d8f5772a0097acf7eae3b877ad000b","observation_id":"2c1c721d-9c20-45fc-b7a4-23053267d777","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Point transformer v3: Simpler faster stronger","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:b619f5b38d1a13a6151461639aee730688c96469c1de8f06c4d47b3503cf5d26","observation_id":"480b83b7-4956-4d2a-9970-6a468b0ac836","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Semi-supervised 3d object detec- tion with patchteacher and pillarmix","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:c150710a3fc4010e794a4da9969333ea201e1962dba3f08a53e62d9ec8f161a5","observation_id":"d8066383-2211-4dc9-bbfb-c97339ca5a40","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"PolarMix: A Gen- eral Data Augmentation Technique for LiDAR Point Clouds","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:a5e86d57c849135356bd2f2d095d1b6ce34f425ed500dad85c1d96ebea68b359","observation_id":"042b5f22-2be5-4686-a733-b4dfd00f574d","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:5bc4f1c262c6f1f9eaaba3d8c376792891bdd76183f151318f109d42f1c1876b","observation_id":"76d0be64-071c-4c5e-bdda-3a55714c3f3f","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"RPVNet: A Deep and Efficient Range-Point-V oxel Fusion Network for LiDAR Point Cloud Segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:1db3744b73902ac490cdc2278c73a66da279c642db4ebb053c168d839cc7a66b","observation_id":"e8ccf111-bdb5-4031-bce0-eb48c547b525","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Mul- timodal learning with transformers: A survey.IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:1526dae531d5450911c90ac367ed0774994bd7cc6406c8107425dd3d51c55602","observation_id":"dfb41892-effe-46c6-9cd4-0a99497eafd7","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:fd73ba160fd9c52cc90072df10fb98f2b728a9769543099f5f36fd08967aa126","observation_id":"a87266f2-129a-4873-aec9-32724deb4d91","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Efficient Point Cloud Segmentation with Geometry-Aware Sparse Networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:8a4af8ee55da67538f965fd192243f6797e4387fc94c0af89501f254001799a8","observation_id":"613a33d1-2689-4989-81a1-f334367d9365","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Litept: Lighter yet stronger point transformer.CVPR, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:60ed33fc3950c37d8af175114248b225bcff44e80127f49ecd32837f0a4798db","observation_id":"6c5e1588-baf1-4419-84a7-8c02136b3d1c","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Scaling vision transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:19aba40e6dec0c6a8c18234129cf488d72e305d13931a7487afc989e4169f57b","observation_id":"2767a850-0d6b-4254-856b-2346a05b6508","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Deep FusionNet for Point Cloud Semantic Seg- mentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:48eef67de3ebff532a9c382a8e35dc326751cd0b963ac0ace52e7dc8fd3eb15c","observation_id":"24176d89-68c1-4355-9de6-78417d168bca","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"PolarNet: An Improved Grid Representation for On- line LiDAR Point Clouds Semantic Segmentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:b033df4b75a04ebcd38f59f2966582d79d32c42729db5fa227579fb9f488ef52","observation_id":"cbeb37af-3491-4003-a14b-f8a32f24eb16","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Torr, and Vladlen Koltun","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:b6edd3140cb65ac6b6b41fdb5d8ada65257203971e3ac267f7992298b179f200","observation_id":"764c795d-531c-4c8e-a5ce-0dbaab493f35","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"SV ASeg: Sparse V oxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:bab73c8139b53d0cdb9174fbb981f072d6bf722ae994a729fc64eb1a4a3d5040","observation_id":"efc75ac5-ee81-4a2d-8245-4d8306008814","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","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":"2026-06-28T22:35:40.495590Z","title":"Cylindrical and Asymmetrical 3D Convolution Net- works for LiDAR Segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-28T22:35:40.495590Z"},"links":{"citing_paper":"/paper/2605.31177"},"observation_digest":"sha256:9b1b0df442fcb823f9aa8fa70a8a29d332765500c28a2d04e187c5eb6e992e69","observation_id":"c3674a73-f360-4ae1-98db-7accc07f904e","resolution":{"observed_at":"2026-06-28T22:35:40.495590Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.31177","last_updated":"2026-05-29T11:47:02Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T11:55:59.827362Z","submitted_at":"2026-05-29T11:47:02Z","title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":59,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":63},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2605.31177."}