{"as_of":"2026-08-08T07:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b5eb0655213dc05822d18ade1b52dff8e55ca87d817fddefcf773c34e7ca8970","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":"2026-08-07T14:44:48.961982Z","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/2505.17783/citation-record","integrity":"/paper/2505.17783/integrity","json":"/paper/2505.17783/citation-record.json","paper":"/paper/2505.17783"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2304.08466","last_updated":"2023-04-17T17:42:29Z","snapshot_observed_at":"2026-07-06T15:16:38.500909Z","submitted_at":"2023-04-17T17:42:29Z","title":"Synthetic Data from Diffusion Models Improves ImageNet Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08466","snapshot_observed_at":"2026-08-07T14:44:43.285963Z","title":"Synthetic data from diffusion models improves imagenet classification","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.285963Z"},"links":{"cited_paper":"/paper/2304.08466","citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:90a2341d2c036b89c2f2dea1f48186a7de4142dfba3ad599ef56138162817484","observation_id":"908a32c3-5df2-44d1-99cc-19623003f501","resolution":{"observed_at":"2026-08-07T14:44:43.285963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:58.807852Z","title":"Segmentor: Obtaining efficient operating room semantics through temporal propa- gation","venue":null,"work_id":"65adc6f1-a570-4871-8707-1cd8d1e0f3a5","year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.339881Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:ab416383cb2bfad55f2ad436c4ee63d9d4fc690f41fe5ff87328f4601cb2ab05","observation_id":"5324f8ea-2549-4c04-a5cf-696456d5c4bf","resolution":{"observed_at":"2026-08-07T14:44:58.868506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:58.637311Z","title":"Shape self-correction for unsupervised point cloud understanding","venue":null,"work_id":"436e1862-841c-4bee-a919-bbc9a2f91efe","year":2021},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.399640Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:9763f1828a55dec91c6c2c1b44c782f14375c0c638057445479bc3ae7f83fd43","observation_id":"b7085a37-dbbd-4880-9be9-2d4819e0af55","resolution":{"observed_at":"2026-08-07T14:44:58.733737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:58.367164Z","title":"Bae-net: Branched autoencoder for shape co-segmentation","venue":null,"work_id":"fae42f3b-efff-4a3a-9182-ab943b25c1f0","year":2019},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.463241Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:454ce20a23a21d5ae659db35ef39fe626e7ff97cf4d2411373ecb4e1ed6d2301","observation_id":"df9267f8-af71-44b2-956e-46c263eaa0f2","resolution":{"observed_at":"2026-08-07T14:44:58.497806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:58.110848Z","title":"Sspc-net: Semi-supervised semantic 3d point cloud segmentation net- work","venue":null,"work_id":"b3a746cc-79ab-4186-8736-ee5347fb7588","year":2021},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.530968Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:f03762660270dc2422b2bbaeef26953e33cc5e6fb47b61a3512a70f29c13fa33","observation_id":"1c05b64b-024b-4fe3-8a1d-31654c2996ed","resolution":{"observed_at":"2026-08-07T14:44:58.229741Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2311.11106","last_updated":"2024-03-11T07:06:29Z","snapshot_observed_at":"2026-07-06T16:49:29.569336Z","submitted_at":"2023-11-18T15:44:57Z","title":"ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation","version":2},"cited_work":{"arxiv_id":"2311.11106","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.11106","snapshot_observed_at":"2026-08-07T14:44:49.657855Z","title":"ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation","venue":"cs.CV","work_id":"c94bbd0c-dc02-4256-a3fe-ee15e6942870","year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.604568Z"},"links":{"cited_paper":"/paper/2311.11106","citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:6c6cdeb0a2625c1478ccd3797ca3c938a74a62c6f9adcc8b7861cf9a6bd572ee","observation_id":"0a79daa3-9893-4987-aaeb-657d3c7a6c7d","resolution":{"observed_at":"2026-08-07T14:44:49.788213Z","resolver_source":"local_arxiv","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":"2026-08-07T14:44:43.675583Z","title":"Generative adversarial networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.675583Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:7f175bed341be415577c3447e39cf0f825fc20197b084d843c3b7ea8bf92989c","observation_id":"d5bef57e-0ae8-48aa-9d60-c92596fcc203","resolution":{"observed_at":"2026-08-07T14:44:43.675583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:57.811552Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"401ea542-2611-4e65-91d3-87d932205244","year":2016},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.732944Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:4ebc9cdada9071c3db7c2a4197e3ed233fa6184c1bf37180c661f3140f59a467","observation_id":"44cfbef6-e0c3-4b05-8788-a4de1cfac49f","resolution":{"observed_at":"2026-08-07T14:44:57.916746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2210.07574","last_updated":"2023-02-15T06:26:38Z","snapshot_observed_at":"2026-07-06T14:05:10.100398Z","submitted_at":"2022-10-14T06:54:24Z","title":"Is synthetic data from generative models ready for image recognition?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07574","snapshot_observed_at":"2026-08-07T14:44:43.815200Z","title":"Is synthetic data from generative models ready for image recognition? arXiv preprint arXiv:2210.07574, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.815200Z"},"links":{"cited_paper":"/paper/2210.07574","citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:d228258feccf4d77ff694b1cd14cb52f739ab17804994cfa50a31d4aeef161f9","observation_id":"67b64720-6b74-4e83-ae1f-fbab2eda55dd","resolution":{"observed_at":"2026-08-07T14:44:43.815200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:43.886188Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.886188Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:7677316d230673d147cd2bc60a8bca1c7c298e0eca2a121d12bd8f2ebd0200c6","observation_id":"a26c59db-5215-4473-a195-95b84b70498b","resolution":{"observed_at":"2026-08-07T14:44:43.886188Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:57.573952Z","title":"Squeeze-and-excitation net- works","venue":null,"work_id":"e90a7d75-33f5-4321-9db2-ea4bcea0f9b7","year":2018},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:43.956288Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:0f0a4e17e3b233660fd2ae8811983c1ad57e0a1a01bcb232352027d4d70c3d6e","observation_id":"b5af4a7d-ee3c-4e1c-9050-d1e563a3579e","resolution":{"observed_at":"2026-08-07T14:44:57.668913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:57.261495Z","title":"Sqn: Weakly-supervised semantic segmentation of large-scale 3d point clouds","venue":null,"work_id":"36b3b18e-2d5e-4b94-afb7-89fb340b38be","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.022108Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:612ae9a6654f751765d20ab37c66040d4df12ffe443caa1d02802f8553e128d1","observation_id":"9d23dfb7-2077-44af-a2c6-6dcf1a58c57d","resolution":{"observed_at":"2026-08-07T14:44:57.441730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:56.958285Z","title":"Lpcg: A self-conditional architecture for labeled point cloud generation","venue":null,"work_id":"26579938-f8f5-4541-aede-eaa42ac238cc","year":2025},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.089134Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:0076c264e3b97f011f942c2a9dc36d5892bd9d6682fd1264963d707a62413efd","observation_id":"ea65a137-d066-4abd-9307-2dcda95e5b61","resolution":{"observed_at":"2026-08-07T14:44:57.073536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:56.782862Z","title":"Guided point contrastive learn- ing for semi-supervised point cloud semantic segmentation","venue":null,"work_id":"eb315cc3-8ad0-4b57-b9a3-40a5e353a204","year":2021},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.157487Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:22f8696338e401c9a47222d91041635f3656025c5323722d247fd6c22da1ee5b","observation_id":"5b6c3062-b99d-4402-9d1d-73ccaebfa60d","resolution":{"observed_at":"2026-08-07T14:44:56.851983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-07T14:44:44.208038Z","title":"Elucidating the design space of diffusion-based generative models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.208038Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:a13cee456c799aa02f42eaff61486263bbdd6e80fdb2b1d5699e646a0c248c8a","observation_id":"398c414f-e97c-4f39-a97c-120c0e9f9575","resolution":{"observed_at":"2026-08-07T14:44:44.208038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:56.569356Z","title":"Semi-supervised learning with deep gen- erative models","venue":null,"work_id":"14b05173-e7ea-42ed-8f26-3db2bc296e9e","year":2014},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.266342Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:a30b5a6928f8d8c963b5860589847a58db7b771f79d68f0d14201541c3d75a5b","observation_id":"699dc281-0c32-4650-9cdd-6a9b5440e1b2","resolution":{"observed_at":"2026-08-07T14:44:56.655995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:56.367178Z","title":"3d- vfield: Adversarial augmentation of point clouds for domain generalization in 3d object detection","venue":null,"work_id":"2bf0fca8-8a12-480a-9cb7-e36e51837a09","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.345612Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:8dce430bd98aeee4b6da2669848e0ac8afba0adc099e4927447f623103cca751","observation_id":"52af5587-5f78-4049-8b39-2e87dc9eeaf9","resolution":{"observed_at":"2026-08-07T14:44:56.484178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:56.206734Z","title":"3d adversarial augmentations for robust out-of-domain predictions","venue":null,"work_id":"e5def06e-1b18-4efd-8640-93a3defea2b9","year":2024},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.349002Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:a646d36c6bdc4f1cfb7670b0508626527587731c7dd09364f86857a39f8efdaa","observation_id":"30f14229-29cd-4035-a690-a7623bd4000e","resolution":{"observed_at":"2026-08-07T14:44:56.255936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:56.035082Z","title":"Pseudoaugment: Learning to use unla- beled data for data augmentation in point clouds","venue":null,"work_id":"5af9fcd0-046b-4897-974e-320a7b4ea758","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.352530Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:c772f65cb023f6eedda2724b9dd0bd2b42cd7e372ccfdb0c56a740ee4bcfaddd","observation_id":"496b62e7-70ca-43fe-9017-30a496ac3d17","resolution":{"observed_at":"2026-08-07T14:44:56.110466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:55.831411Z","title":"Less: Label-efficient semantic segmentation for lidar point clouds","venue":null,"work_id":"6eafb964-3e1c-4dd3-881b-6887fe8d2062","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.357283Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:6b2bed2d29fe13c53ed207cd6bf8b4c1ff8df738be614e3d15ceef390a11df86","observation_id":"8f567f78-6332-4be0-90f1-d69446931c18","resolution":{"observed_at":"2026-08-07T14:44:55.945443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:55.633323Z","title":"Point- voxel cnn for efficient 3d deep learning","venue":null,"work_id":"7b6d959d-2d9d-4eee-b329-bb11c834b33b","year":2019},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.428820Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:f96e4d50b3e9cb4758d3b26c4f7e34db8c06b6883b64a3935824d22c853ee83c","observation_id":"9df54311-43e5-45b6-b6ef-b041a9aaa7aa","resolution":{"observed_at":"2026-08-07T14:44:55.697319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:55.466029Z","title":"One thing one click: A self-training approach for weakly supervised 3d semantic segmentation","venue":null,"work_id":"32cf0a3e-caaf-4d6f-9690-b97cf95f7691","year":2021},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.534696Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:7fdfa9612fce6f01d66b20630aeb238e024a006990e7341d2d0283234ac6e179","observation_id":"0deda63f-a88e-4b76-80b5-1808d67a287a","resolution":{"observed_at":"2026-08-07T14:44:55.555846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:55.317960Z","title":"Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data","venue":null,"work_id":"383f3cf9-8729-4246-9043-e1f2f0b2a364","year":2020},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.613500Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:030881065d3cbe55227c6d881fcbadd2ddaa5fa16fcb6d63983b20971a1db70c","observation_id":"57e43df7-1af3-460d-ac56-51512157a5d8","resolution":{"observed_at":"2026-08-07T14:44:55.402231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:55.170747Z","title":"Diffusion probabilistic models for 3d point cloud generation","venue":null,"work_id":"e5a16c72-3a7a-4754-a2f0-4ec0f720d876","year":2021},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.695696Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:ad7c966821eec0cd9b4f125b5f4dfcb2acaeabe7042ca2f4d0a5519bb468f016","observation_id":"c7b34dc1-b7e3-4311-b0dd-ad21d57e14c1","resolution":{"observed_at":"2026-08-07T14:44:55.255095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:54.976393Z","title":"SDEdit: Guided image synthesis and editing with stochastic differential equa- tions","venue":null,"work_id":"97feea48-9bde-429c-bcfc-92fe7bee955b","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.798611Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:0eddcd5aada9e94ac9dfec5d83d1a7aa01d5a8b511d3e0ab021193e296697a07","observation_id":"ccba5073-4955-4fc6-ad5f-9ba78a417a39","resolution":{"observed_at":"2026-08-07T14:44:55.068652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:54.760535Z","title":"Partnet: A large- scale benchmark for fine-grained and hierarchical part-level 9 3d object understanding","venue":null,"work_id":"9de7a150-af09-464e-88d0-f7f54c286ab3","year":2019},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.888024Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:b4b62ee5be3c583c0a6da2b5807857a413bb905f159c39840b2c87f87b6d1652","observation_id":"9c26267f-6a4d-4468-88d0-1bbd49959eca","resolution":{"observed_at":"2026-08-07T14:44:54.870241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2006.05278","last_updated":"2020-07-06T17:38:19Z","snapshot_observed_at":"2026-08-07T18:58:50.982040Z","submitted_at":"2020-06-09T14:08:03Z","title":"An Overview of Deep Semi-Supervised Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05278","snapshot_observed_at":"2026-08-07T14:44:44.993294Z","title":"An overview of deep semi-supervised learning","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:44.993294Z"},"links":{"cited_paper":"/paper/2006.05278","citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:a4cdfb93685814c52bcf26cbfbd4305ae992d8291b30a6cf9fd73b123cff6e7a","observation_id":"e263e3c0-cda1-40c8-bd43-8ebc139c3ffe","resolution":{"observed_at":"2026-08-07T14:44:44.993294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:54.570947Z","title":"3d part segmentation on shapenet-part","venue":null,"work_id":"c9e8d249-affa-4429-8496-95358094051b","year":2025},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.088310Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:16069fbeaafc45959e7a7615d85bac190d5cfd921c3bae15907d4cdc9a52794b","observation_id":"7326cf50-4fb8-4543-8c06-b52a242a3912","resolution":{"observed_at":"2026-08-07T14:44:54.629307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:54.374311Z","title":"Lee, Si Hyeon Kim, Yunyang Xiong, and Hyunwoo J","venue":null,"work_id":"2047ffa9-5996-4c67-a085-0fdc3d126371","year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.213422Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:53a481be9320016b27c70084286aa61cc5aba5498c1367f17d70ce092242dca9","observation_id":"5167ad08-5464-49a4-93cd-1d31de8a0881","resolution":{"observed_at":"2026-08-07T14:44:54.461328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:54.255875Z","title":"Qi, Hao Su, Kaichun Mo, and Leonidas J","venue":null,"work_id":"748c1d1c-e0cf-47f8-a2ad-374418525c22","year":2017},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.324415Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:994835ff26d1f68e37bbd6c20dcc3abb42578d6de38d892d5ced4e9ac530ccd4","observation_id":"d226be92-e089-4756-ba28-8b7dd0db1816","resolution":{"observed_at":"2026-08-07T14:44:54.309352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:54.112294Z","title":null,"venue":null,"work_id":"b7496669-3512-413c-8576-9d43b36c45d6","year":2017},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.432092Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:ea5fe0bdb29c92640d15729417d3950fefbdd08aa02dc3160279b3f2c6d327cb","observation_id":"e1faae5d-ece3-408c-8088-80cd6f299f8b","resolution":{"observed_at":"2026-08-07T14:44:54.160144Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:53.883456Z","title":"Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining","venue":null,"work_id":"46fd21df-2050-423e-8f6e-39873c930ab3","year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.555134Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:8f5c47df57b76bb00c49e53a161de05bf5ced3779ea0258e9237223135907cb0","observation_id":"7e4557d5-f599-4058-bda5-6eb2e0b11303","resolution":{"observed_at":"2026-08-07T14:44:53.996863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:53.688017Z","title":"Bringing masked autoencoders explicit con- trastive properties for point cloud self-supervised learning","venue":null,"work_id":"f5a15aa2-c7ca-4499-ac01-323dbea57511","year":2024},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.678397Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:478468825e017062bc510aab9412e9dba5f8f5f1d6465b6f54aa5b80ae2ebdb1","observation_id":"656bc860-7f89-4295-84cb-d77cbf58196a","resolution":{"observed_at":"2026-08-07T14:44:53.795862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2303.14207","last_updated":"2024-03-12T15:40:08Z","snapshot_observed_at":"2026-07-06T15:07:45.408464Z","submitted_at":"2023-03-24T18:00:15Z","title":"DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.14207","snapshot_observed_at":"2026-08-07T14:44:45.774801Z","title":"Diffuscene: Scene graph denoising diffusion probabilistic model for generative indoor scene synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.774801Z"},"links":{"cited_paper":"/paper/2303.14207","citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:8b13d73bb27404ac7fcf56d41f218cfe1715a0f3416d059d5817c4dd40b958b0","observation_id":"b07a29d6-faf2-4a7f-a672-93be99def430","resolution":{"observed_at":"2026-08-07T14:44:45.774801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07944","last_updated":"2025-06-10T20:01:59Z","snapshot_observed_at":"2026-08-07T10:59:13.865982Z","submitted_at":"2023-02-07T20:42:28Z","title":"Effective Data Augmentation With Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.07944","snapshot_observed_at":"2026-08-07T14:44:45.883274Z","title":"Effective data augmentation with diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:45.883274Z"},"links":{"cited_paper":"/paper/2302.07944","citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:8fdfd86c8a60420b5e4a4a3412e30f5fb03078c86c784ecffa4c524f1f7d05c5","observation_id":"c126979a-2156-42ea-944f-99c80c02e752","resolution":{"observed_at":"2026-08-07T14:44:45.883274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:53.489199Z","title":"Few-shot learning of part-specific probability space for 3d shape segmentation","venue":null,"work_id":"3b144c67-db08-46f0-8b6f-03fd21875f47","year":2020},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.050474Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:f4f9b4c7d641e372dfcb5afe73f43b02c7cf8467c390fa9394bea20f8a62f150","observation_id":"e84e47d6-53ca-4c49-8ed3-de43b8b0ce14","resolution":{"observed_at":"2026-08-07T14:44:53.591285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:53.319739Z","title":"Group normalization","venue":null,"work_id":"7109c619-9faf-41f8-a390-5ff4b3923531","year":2018},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.180369Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:f89489439565906d0117b7ef1e0d37205e75fbc616b740cd8a17a6cd51f69ba2","observation_id":"abe37a4a-9b10-44bd-8790-9734c4a7bb58","resolution":{"observed_at":"2026-08-07T14:44:53.377656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-07T14:44:46.300292Z","title":"Pointcontrast: Unsupervised pre- training for 3d point cloud understanding","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.300292Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:8300fb82f26ea3373951b5f52c916467764e51640ad308d5bfe6eac8b6549d5b","observation_id":"6fb8c125-4b94-4199-8dc0-62c56e8527e3","resolution":{"observed_at":"2026-08-07T14:44:46.300292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:53.135416Z","title":"Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels","venue":null,"work_id":"029e58f9-149c-48ed-9251-e4ef8aa546ab","year":2020},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.453525Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:95992ce6e96db7bd5887f1e504d0910759b12cf8af3ac6b2cb2ae73959650cb1","observation_id":"477accd8-cef6-4ba3-bcb9-184bd1b766e0","resolution":{"observed_at":"2026-08-07T14:44:53.211173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:53.005957Z","title":"An mil-derived transformer for weakly supervised point cloud segmentation","venue":null,"work_id":"2a7849a7-8ccf-4a27-ae8b-99baf97d1da9","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.539185Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:f834a112f34f89f9da9902c8188eeec6fc118e030177d18550c7f26e22b1d369","observation_id":"f8f26687-bb7f-4562-a1d6-f3bfa23aa6c1","resolution":{"observed_at":"2026-08-07T14:44:53.039733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:52.873131Z","title":"Intra: 3d intracranial aneurysm dataset for deep learning","venue":null,"work_id":"081287bd-4471-4d8b-853e-d4e50a9d32f9","year":2020},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.615282Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:0b5eb13f21c377a8d8bdf41e30d4bea235f8b2c03c7f9dd4e6064c2dcaaba7f8","observation_id":"172de9f2-36db-4179-b201-d96f29e6c033","resolution":{"observed_at":"2026-08-07T14:44:52.948334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:52.713551Z","title":"Yi, Vladimir G","venue":null,"work_id":"eddb4ef2-e139-4fc0-85d1-b64297b26c41","year":2016},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.712214Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:b89679a6dd4d24e83448ff0cd429d20329e1a257f76ab63571956248adb2919d","observation_id":"e028d389-f1ff-4a28-a8c1-369ac4987b78","resolution":{"observed_at":"2026-08-07T14:44:52.771790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:52.502816Z","title":"Diffusion models and semi-supervised learners benefit mutually with few labels","venue":null,"work_id":"ca760e87-7e6d-401a-b5fa-cee1c69e7c48","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.801013Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:93d33c7f46f580dc46ab8dc8772d0aad92cf512dd4dedbfc9e573c69807c8429","observation_id":"444c5e43-1ea0-4ead-a32e-1175826219e8","resolution":{"observed_at":"2026-08-07T14:44:52.616442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":{"arxiv_id":"2210.16023","last_updated":"2022-10-28T09:53:05Z","snapshot_observed_at":"2026-08-04T09:56:22.727462Z","submitted_at":"2022-10-28T09:53:05Z","title":"LegoNet: A Fast and Exact Unlearning Architecture","version":1},"cited_work":{"arxiv_id":"2210.16023","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.16023","snapshot_observed_at":"2026-08-07T14:44:49.363277Z","title":"LegoNet: A Fast and Exact Unlearning Architecture","venue":"cs.LG","work_id":"b2c4e914-b061-4f82-8608-0aae59659f74","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.886583Z"},"links":{"cited_paper":"/paper/2210.16023","citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:cf5aed7bdf4ef5d3c72c92b47d4bfe335069e174c3bf707745831478ba08b6ec","observation_id":"e62dc691-4bcc-4685-8cc3-ed6d96e1760d","resolution":{"observed_at":"2026-08-07T14:44:49.479506Z","resolver_source":"local_arxiv","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:52.358452Z","title":"Lion: Latent point diffusion models for 3d shape generation","venue":null,"work_id":"b5b0231c-0dc8-40c6-be32-039c72421402","year":2022},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:46.950890Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:723f8d9468bb30dc2fe23e5c4eaa52918697347933d14f9552c2646ad4f9ef88","observation_id":"274834e7-a1f2-44df-95e4-5d0d843daf26","resolution":{"observed_at":"2026-08-07T14:44:52.430124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:52.217290Z","title":"Echoscene: Indoor scene generation via information echo over scene graph diffusion","venue":null,"work_id":"141ff094-6e78-43ff-830d-85ecaedb1470","year":2024},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:47.044684Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:ef308c854d773bb36917024c78294655da19d816ace8c9ae966639eafd9a4e38","observation_id":"894c3bba-770b-4d7a-94f1-1f38eb7b50b7","resolution":{"observed_at":"2026-08-07T14:44:52.275668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:52.101592Z","title":"Commonscenes: Generating commonsense 3d indoor scenes with scene graphs","venue":null,"work_id":"90026b17-31d4-4bd9-b604-364e010e803a","year":2024},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:47.139683Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:50007bd0fcc032d071149a73aa4f5d15cd905c3f1f7bb6460c16d5f1600ccc93","observation_id":"d289a3ca-b72f-4f31-83e1-e3047d2f9940","resolution":{"observed_at":"2026-08-07T14:44:52.151516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:51.938744Z","title":"Point transformer","venue":null,"work_id":"1280e01b-c769-4477-939f-d61552208227","year":2021},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:47.323290Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:687278d797ccd364298bddfbc85835c34487e2b03743b09910cbd68246fb5de3","observation_id":"b076b1b9-c5ea-4147-88e2-ed76f49a0402","resolution":{"observed_at":"2026-08-07T14:44:52.014368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:51.809159Z","title":"Toward understanding generative data augmentation","venue":null,"work_id":"d4962d9c-68a6-452b-9055-ba5fca618b0c","year":2024},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:47.484912Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:4ca4332f75ff5ba3ff00d56203f43b39eb870397a07232b341fd62931363eefa","observation_id":"7c5d0faf-da29-4f93-99a1-a22f5c8368e0","resolution":{"observed_at":"2026-08-07T14:44:51.876397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:51.688590Z","title":"3d shape generation and completion through point-voxel diffusion","venue":null,"work_id":"50cf07ca-f20f-4187-93ea-9a5d35daa0a2","year":2021},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:47.609921Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:5db65da76a94b6ff5b075a6db05553fc751dc78a01967aa5e8e99b3ead8d5158","observation_id":"723fde06-2ae7-4647-b928-ba8d16f571e2","resolution":{"observed_at":"2026-08-07T14:44:51.756237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:51.541533Z","title":"Ipcc-tp: Utilizing incre- mental pearson correlation coefficient for joint multi-agent trajectory prediction","venue":null,"work_id":"d34ed115-6408-4d34-bbde-aad322fdb598","year":2023},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:47.738053Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:0500d2b3314089c8d53f28416e84b3a5325687cc71cf4b63c279109c91d7823f","observation_id":"c9d64b72-df2a-4da3-94c7-3c951af470a1","resolution":{"observed_at":"2026-08-07T14:44:51.599233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:51.365918Z","title":"Multi-vehicle trajectory prediction and control at intersections using state 10 and intention information","venue":null,"work_id":"4f50571d-82b9-4adb-81ca-8670af647f6e","year":2024},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:47.887290Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:82cd3eb38fe08098195aa08fa3c232d0b28d49fd256d0faee71345574e70fefd","observation_id":"4455088e-bc5a-46be-9ef8-b1496554932c","resolution":{"observed_at":"2026-08-07T14:44:51.470016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:51.242122Z","title":"Sealion: Semantic part-aware latent point diffusion models for 3d generation","venue":null,"work_id":"3839d908-e2a7-4235-b236-7469148e17ee","year":2025},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.037820Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:727559c820213f3a7acd9b1550bd538722d6af378f304a9d7011c72c0eb66ee1","observation_id":"f2bdfefe-d916-43f8-b83f-41933aed4442","resolution":{"observed_at":"2026-08-07T14:44:51.306536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2505.22643","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:49.135619Z","title":"Spiral: Semantic- aware progressive lidar scene generation","venue":null,"work_id":"6e179ee2-0b00-47d4-be26-7863a01308c9","year":2025},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.161476Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:196d6a7ce613fe434ed23b18904992000ecdf0b0d4408ab69e7516824f9ffda1","observation_id":"9adb9b31-9179-43b9-9c45-cd5e8268fddc","resolution":{"observed_at":"2026-08-07T14:44:49.270950Z","resolver_source":"raw_fallback","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:51.090263Z","title":"Preliminaries","venue":null,"work_id":"f7f35d74-20f4-42ec-97d0-39346524b50b","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.232993Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:d91cd76eef055f628012d7e25bf6d2d2a16cb0acfc620e87baad95d07c59c4d3","observation_id":"85ba70a6-979f-482d-86ad-2ff0a146040e","resolution":{"observed_at":"2026-08-07T14:44:51.175444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:50.951710Z","title":"Experimental Settings","venue":null,"work_id":"b233a9d3-011d-4295-a3ac-641088f862b7","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.327391Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:8206ac0442927997afb06ca0ed0bcd58e879695dfdb4394965d7e427c9cb9f90","observation_id":"97e7ffda-78a6-4651-9c4a-2d0de18de64f","resolution":{"observed_at":"2026-08-07T14:44:51.025101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:50.799171Z","title":"More Experimental Results 12 A.1","venue":null,"work_id":"f10ba5d4-8f87-4b8e-bd1c-fc6e642a9815","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.420098Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:1d15a8152f2076bd756e3b6b53c8cb1e8765d1dfb942045fe50d41fc47fab310","observation_id":"bcf942e6-16f9-48e2-8c0f-b40aeb8fdecc","resolution":{"observed_at":"2026-08-07T14:44:50.874939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:50.664479Z","title":null,"venue":null,"work_id":"cde457eb-44e4-4498-b631-4f6b500d1b4c","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.515257Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:71253958e07f20da6d3d4bb74f7cf8bcfc52c20695825345a78b4307c50751e5","observation_id":"7fbdc3cc-cc9d-4d28-917c-7df194b60347","resolution":{"observed_at":"2026-08-07T14:44:50.717018Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:50.499286Z","title":"GDA for PointNet [30], PointNet++ [31], and SPoTr [29] on IntrA [41] dataset","venue":null,"work_id":"0cd00a28-7cf2-4a16-acac-50f2bac1c2be","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.590787Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:0a520253d5ffa8c00177d89f829ef7e6b88d921204b81ca4a9cc8b9f1d8401cd","observation_id":"d4477e71-3a7e-4b44-8058-8315d5ef6f71","resolution":{"observed_at":"2026-08-07T14:44:50.580796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:50.337926Z","title":"Although the level 2 samples contain artifacts of jittering points or non-uniformly distributed points, it gener- ally maintains a reasonable shape and segmentation labels","venue":null,"work_id":"e364be61-4bc7-458a-9b74-ecc0d9955935","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.689523Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:5fc270ef0a1ed6a8cbf174aa2fa4b4b3a05d8002d61db20afc5e29516d55c34a","observation_id":"c546acb1-e0b6-47c5-9d7f-4dd113aa50ba","resolution":{"observed_at":"2026-08-07T14:44:50.414625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:50.205918Z","title":null,"venue":null,"work_id":"ec33f844-65e6-42e6-8f79-a8a0607f1175","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.773540Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:5a60f15e54330121f55e0713e0ea9fa7eab256211c39303f65f09c1245c500b7","observation_id":"ea2a8dd9-e611-4d55-a500-1361ea2d5af1","resolution":{"observed_at":"2026-08-07T14:44:50.267074Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:50.051404Z","title":null,"venue":null,"work_id":"f3b29722-329b-404b-8e35-5f84a5325857","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.845795Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:e847dc44426d0aa2fc67df39d6656e6d1b2e32d7e3a321d24e498309c95059d5","observation_id":"50903e07-915b-4907-9b1b-eab972c1552f","resolution":{"observed_at":"2026-08-07T14:44:50.126055Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:44:49.905126Z","title":"The segmentation results on cars and airplanes from ShapeNetPart [42] are demonstrated in Fig","venue":null,"work_id":"5d6997ba-93f2-4b10-b82a-5030a5c4237e","year":null},"citing_paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:48.961982Z"},"links":{"citing_paper":"/paper/2505.17783"},"observation_digest":"sha256:e7e683b5860aa9119f679bb922375c2b929611efddaf0be784368efa9a1d97cb","observation_id":"3707b7c4-e08a-4a79-af07-3d9438fbdff1","resolution":{"observed_at":"2026-08-07T14:44:49.968485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2505.17783","last_updated":"2025-08-26T15:45:08Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T14:38:37.515939Z","submitted_at":"2025-05-23T11:56:06Z","title":"Generative Data Augmentation for Object Point Cloud Segmentation"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":3,"verified_fuzzy":47},"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 8 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2505.17783."}