{"as_of":"2026-08-15T18:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b48f7dea8d4fb482b5231da733e972a3408c91ee5e1de2681639a80a190c12c4","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T05:33:48.531558Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T14:52:30.404272Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-10T18:16:17.926450Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"cited_work":{"arxiv_id":"2509.05198","doi":"10.48550/arxiv.2509.05198","metadata_source":"pith","pith_arxiv_id":"2509.05198","snapshot_observed_at":"2026-08-10T18:16:17.926450Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","venue":"cs.CV","work_id":"f0b81069-5f0c-4bf7-a12a-46546932e908","year":2025},"citing_paper":{"arxiv_id":"2608.07106","last_updated":"2026-08-07T11:02:55Z","snapshot_observed_at":"2026-08-15T13:43:00.331136Z","submitted_at":"2026-08-07T11:02:55Z","title":"Synthetic LiDAR Data Generation and Deterministic Downsampling for Point Cloud Classification on the Edge","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T14:52:30.404272Z"},"links":{"cited_paper":"/paper/2509.05198","citing_paper":"/paper/2608.07106"},"observation_digest":"sha256:ef341525a153e97e30304cf25cec1e2640e4a19bfe1abb795115625e1378bd7a","observation_id":"1c6baac9-49bb-486f-a330-172972653903","resolution":{"observed_at":"2026-08-10T14:52:30.822787Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.05198/citation-record","integrity":"/paper/2509.05198/integrity","json":"/paper/2509.05198/citation-record.json","paper":"/paper/2509.05198"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:33:48.360511Z","title":"Discovering new shadow patterns for black- box attacks on lane detection of autonomous vehicles","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.360511Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:2f37cb01225d405b0694836eea694cb1dbd68b17d783946a4cb0db7834648362","observation_id":"38c64dd6-017e-472e-95ec-912ac25ff459","resolution":{"observed_at":"2026-08-05T05:33:48.360511Z","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":"2026-08-05T05:33:49.178165Z","title":"Avoiding the crash: A vision language model evaluation in critical traffic scenarios","venue":null,"work_id":"14bbd6ac-cb39-42d5-9179-4f3ed25acf2c","year":2025},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.364952Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:f604c40fa43658dec2e319038ea159dfbd32f3f82d07ef0644c2d24a73612f94","observation_id":"bcf270db-a83b-423b-ae2b-9da4b435200a","resolution":{"observed_at":"2026-08-05T05:33:49.182107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.164670Z","title":"Grasping pose estimation for scara robot based on deep learning of point cloud","venue":null,"work_id":"74feb1f3-bbf9-46b5-be8a-d873f4fe9363","year":2020},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.369575Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:42a572938a7abaf7cc5ae65b71736598eb5768f14a33fc6a2b0c768ecf4d2232","observation_id":"552b0fca-30a7-40d9-b80f-b229062b5f2f","resolution":{"observed_at":"2026-08-05T05:33:49.169169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.151973Z","title":"Long-term estimation of human spatial interactions through multiple laser ranging sensors","venue":null,"work_id":"4e2c96b1-eac4-40a0-b8fb-b84a62c16c17","year":2014},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.373801Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:06b8024b8db582d69eb5e609024a0d125af222a0b299028c364ff68f3119fa75","observation_id":"aab0d3ad-ce30-463e-8530-c64030943387","resolution":{"observed_at":"2026-08-05T05:33:49.156009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.139152Z","title":"Bim-based registration and localization of 3d point clouds of indoor scenes using geometric features for augmented reality","venue":null,"work_id":"7f981a4a-e785-4cb3-bb23-15375ca21e01","year":2020},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.378605Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:c50d07150a31d0b8110e4caa7d9cd689d6de276d118b36043bfee8b927369eb5","observation_id":"0306cc87-6dbd-4c8a-850b-589e61b8e5fa","resolution":{"observed_at":"2026-08-05T05:33:49.143225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.125896Z","title":"A mobile augmented reality system for the real-time visualization of pipes in point cloud data with a depth sensor","venue":null,"work_id":"bbcd3ddd-7905-4e28-9e3f-1b46cbdc9bd7","year":2020},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.382883Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:21c0f5beb14c1d24d77b8f5c91a0fa71d16cde199280594f019c763e2d948891","observation_id":"0d7e90b7-9cdc-4da7-a232-8646c0316dba","resolution":{"observed_at":"2026-08-05T05:33:49.130540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.112159Z","title":"Point cloud generation using deep adversarial local features for augmented and mixed reality contents","venue":null,"work_id":"02ba4035-ac6a-41ed-9ee9-855e858d9d8d","year":2022},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.387637Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:47264225f8c74be212dd36509e7aec099de50c9fd6b4b83d5e2362e10136cf30","observation_id":"48cae59b-d19c-4b1a-96b7-a33cc4b52f53","resolution":{"observed_at":"2026-08-05T05:33:49.116663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.098341Z","title":"Graph neural networks in point clouds: A survey","venue":null,"work_id":"fe7dbfc1-8e97-4bce-84cb-c07635e465d0","year":2024},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.391872Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:652bdacdd6ba41bd06b5f30f11ebb7a8aebf77348f5e35083b4a004096fe1c6f","observation_id":"2e8b1ba8-5e23-41d6-b566-d1461ca4971d","resolution":{"observed_at":"2026-08-05T05:33:49.102727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.083569Z","title":"3d shapenets: A deep representation for volumetric shapes","venue":null,"work_id":"1608ed2b-5dc1-48cf-9780-5d0080b4f425","year":2015},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.396393Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:7483f5e3886b1f1d59cbf74a8ef7edbabb61e610cfa0c32914c097b48c6751a4","observation_id":"67da2eac-18b3-4a8f-ad5e-9348ee54b718","resolution":{"observed_at":"2026-08-05T05:33:49.088256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07123","last_updated":"2022-11-29T07:07:37Z","snapshot_observed_at":"2026-08-14T13:53:24.804937Z","submitted_at":"2022-02-15T01:39:07Z","title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.07123","snapshot_observed_at":"2026-08-05T05:33:48.400534Z","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":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.400534Z"},"links":{"cited_paper":"/paper/2202.07123","citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:61ec51136fa79c0118ac0e071ad7393c8db039d88592ac68066400cbb7228e27","observation_id":"a83d3a8f-6bf9-44b8-b75b-dc6c1e8ec34b","resolution":{"observed_at":"2026-08-05T05:33:48.400534Z","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":"2026-08-05T05:33:49.069558Z","title":"Attention-based point cloud edge sampling","venue":null,"work_id":"c9d55e0f-7c1a-43b9-a74a-4c8b9b54cff2","year":2023},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.404827Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:622f698f41f7c99ceca93ca53cb59298a6642c86ebf9a10e34e02590cbb41bde","observation_id":"f78226bf-533c-4a10-8f29-e823e33acb52","resolution":{"observed_at":"2026-08-05T05:33:49.073674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.055855Z","title":null,"venue":null,"work_id":"371152f7-4b29-4f98-865b-50d6ddbdd1c1","year":2024},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.409058Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:ec8b3090c2119eafd0922d95c4dbf028e9fdcec995b85c67bbf760aee91eae0b","observation_id":"5452a421-b2e6-446b-a7a6-15ed2cf884af","resolution":{"observed_at":"2026-08-05T05:33:49.060143Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.041164Z","title":"Deep learning for lidar point clouds in autonomous driving: A review","venue":null,"work_id":"38dd85c8-f4d1-4680-bff5-817f15730217","year":2020},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.413227Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:1d2f79863538c76f94d37cdadf3b3d85a28acbbbbb0786b811d23503b9b9ed76","observation_id":"6b8bb54b-0549-48b5-961b-2599589842a2","resolution":{"observed_at":"2026-08-05T05:33:49.045734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.027333Z","title":"Deep learning for image and point cloud fusion in autonomous driving: A review","venue":null,"work_id":"259490a5-d67e-4f32-9402-ffc6fa3400ad","year":2021},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.418163Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:73335459fe4e3d71c7e9be97dea7720edc2aba94a6280d17fd344a3b425b7229","observation_id":"4d5a062c-7fd1-43df-9bc4-9acf72759f94","resolution":{"observed_at":"2026-08-05T05:33:49.031561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:49.010875Z","title":"Lidar point cloud compression, processing and learning for autonomous driving","venue":null,"work_id":"c0ab1531-73b1-45b5-943d-3234601ec2f3","year":2022},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.422142Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:ec28faa34d00bf0b113994487f08a0df1663a99705ce9cd55a4e55724ec5d363","observation_id":"86482678-6708-4abe-9b2b-525ef65b5376","resolution":{"observed_at":"2026-08-05T05:33:49.016415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.995726Z","title":"Robotics dexterous grasping: The methods based on point cloud and deep learning","venue":null,"work_id":"f981a60b-c9d7-4b15-804a-e706d08291f3","year":2021},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.425917Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:bc6b8d09fe445decd94ab90d6f91a8249749956ca3257198fd029e9bfb26d7bc","observation_id":"25c21cef-c30c-49c7-81f6-4bbc6ac32438","resolution":{"observed_at":"2026-08-05T05:33:48.999989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.980699Z","title":"Trajectory planning and optimization for robotic machining based on measured point cloud","venue":null,"work_id":"9aa2ed18-88a3-4ef4-a2d5-967689225cf6","year":2021},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.429565Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:da294457b334a05a62d1bf392812678df9e95e8e0507216561cda871e14245fc","observation_id":"7643bd65-e522-477a-9207-165779005f07","resolution":{"observed_at":"2026-08-05T05:33:48.985479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-14T23:08:26.232837Z","submitted_at":"2015-12-09T19:42:48Z","title":"ShapeNet: An Information-Rich 3D Model Repository","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03012","snapshot_observed_at":"2026-08-05T05:33:48.433389Z","title":"Shapenet: An information-rich 3d model repository","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.433389Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:4a5c5e51df6e96effaf99028b4e866359e1897c89625fd193e1eacf196d6767e","observation_id":"8c453f21-1112-459d-9d37-b870951145bc","resolution":{"observed_at":"2026-08-05T05:33:48.433389Z","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":"2026-08-05T05:33:48.965073Z","title":"Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data","venue":null,"work_id":"77afdc65-c1ff-49ae-b151-163b28717533","year":2019},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.437325Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:25d3e75107ddee9163a36d906ec29b416a87fec8a0eaf6d716e1973518829121","observation_id":"d0d60827-900f-4c87-ab9a-5195b7e1cb0b","resolution":{"observed_at":"2026-08-05T05:33:48.969809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.950021Z","title":"3d semantic parsing of large-scale indoor spaces","venue":null,"work_id":"a88c7232-799a-4203-bf12-a0006969d425","year":2016},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.441486Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:4f8c302fe69fc84b37e31dbdf4092ca989ed092346ad119f57273a0a8e803d3f","observation_id":"71dbb756-31ef-4464-9978-ad93a5fb1c60","resolution":{"observed_at":"2026-08-05T05:33:48.954401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.935741Z","title":"Sun rgb-d: A rgb-d scene understanding benchmark suite","venue":null,"work_id":"a69d7de4-6540-4c80-ac6d-d852fea346d6","year":2015},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.445893Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:27672a16dab937d2fc2d1e7cf4092b1bb0248217d754aa7599df640465555132","observation_id":"175f613b-616c-4157-adb0-8a8ea51b5e6c","resolution":{"observed_at":"2026-08-05T05:33:48.940249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.450151Z","title":"nuscenes: A multimodal dataset for autonomous driving","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.450151Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:6b8985cab8c6cb0dd6cc959d42de8748b27ba748a6fc0d071f2905ae2afa484f","observation_id":"01743c48-8ceb-447b-9eb9-f431c8c82588","resolution":{"observed_at":"2026-08-05T05:33:48.450151Z","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":"2026-08-05T05:33:48.910271Z","title":"Vision meets robotics: The kitti dataset","venue":null,"work_id":"9aaf492c-8d24-4372-b092-5c66b95ca289","year":2013},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.454102Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:fcbbb5993bed218e0df81d8f5b3d672b61c3e569220e7e514e972df61745099a","observation_id":"fbf3b807-3a79-4817-8be6-659788a59941","resolution":{"observed_at":"2026-08-05T05:33:48.914740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.895447Z","title":"V oxelnet: End-to-end learning for point cloud based 3d object detection","venue":null,"work_id":"72c82042-6970-4dd3-8c49-2480c0050748","year":2018},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.459250Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:4daa7ebb208e08e1d0e0d91e625fb67b65be070eb93a910c071469eb376d2155","observation_id":"126effc7-89fe-4fe0-808e-2514e5cba4b1","resolution":{"observed_at":"2026-08-05T05:33:48.899969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.880840Z","title":"Projection-based point convolution for efficient point cloud segmentation","venue":null,"work_id":"65c95f4d-763c-4268-aed2-e7b491a40893","year":2022},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.463324Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:f14327aae3a3727dd5100fe274d02fafd3144f28d62ea7436870221b45ab2184","observation_id":"9d01c75a-97a7-4f50-9c1b-24889a4f5dee","resolution":{"observed_at":"2026-08-05T05:33:48.885316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.866930Z","title":"Semantic segmentation of 3d lidar data using deep learning: a review of projection-based methods","venue":null,"work_id":"c4448e92-57d2-433a-9405-f01515b6e357","year":2023},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.467335Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:3fef8a44dbd068a8f4be413da1f5bd50a052f0aba2eb8256b553b62c4853bc32","observation_id":"2f53e43b-b709-4ea4-b11e-63ae5ab7a2fa","resolution":{"observed_at":"2026-08-05T05:33:48.871310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.852666Z","title":"A fast and efficient projection-based approach for surface reconstruction","venue":null,"work_id":"5ed0d56e-8127-4a80-b20c-50939bacac62","year":null},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.471484Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:9e723a438038bd64168f155dfc38e1dfdaea069c13fa467d28871bd17c8fa620","observation_id":"b14b72fb-df33-4bc4-8d2d-edd8f3d0b08c","resolution":{"observed_at":"2026-08-05T05:33:48.856877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.838282Z","title":"Back- propagation applied to handwritten zip code recognition","venue":null,"work_id":"95fa4b4e-3f8e-496d-b504-1c0685c9e90a","year":1989},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.476337Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:7ed021ad165f83878cdc7144a9633af0263ef4b6a875c4d1aba665403887c8fb","observation_id":"495df9a1-6f34-4703-866a-a467cc4365ac","resolution":{"observed_at":"2026-08-05T05:33:48.842743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.480708Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.480708Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:34edc2112c72d1f1688507c051022254cfd2c80cd367570a390735ef491a6a4b","observation_id":"c2441e1e-b06e-4746-a084-5a027f252a4f","resolution":{"observed_at":"2026-08-05T05:33:48.480708Z","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":"2026-08-05T05:33:48.814611Z","title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","venue":null,"work_id":"00dc28ff-f094-413d-9705-101e2809fd45","year":2017},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.484578Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:dd8da07a7ec72f9a73b78cc9a840dfb04f9ae2514ddc0e6bb38e0129d094a88b","observation_id":"2549f984-30cc-4a05-9830-336aa2b9719f","resolution":{"observed_at":"2026-08-05T05:33:48.819123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.800475Z","title":"Dynamic graph cnn for learning on point clouds","venue":null,"work_id":"f2414c14-0a37-41e2-a2d6-a02aca22405a","year":2019},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.488540Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:862cfb10ae3e1b76270e1a9d7a8e4ae8702bb1b82dbb7c0c22fbc17d3b452f1a","observation_id":"da6fc4ef-85f2-452d-855a-c627b4cc2265","resolution":{"observed_at":"2026-08-05T05:33:48.804992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.786472Z","title":"Kpconv: Flexible and deformable convolution for point clouds","venue":null,"work_id":"b95afd47-1691-46e3-8e74-704b4908675f","year":2019},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.492670Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:7a670804457e4c28a7df48a7b19ef5b56d23c4b2a46a4ebc92d0583dd9f44046","observation_id":"17adac53-c9d2-4b69-ae61-bbe5189ff69d","resolution":{"observed_at":"2026-08-05T05:33:48.790942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.771287Z","title":"Point transformer","venue":null,"work_id":"0421b77a-19cd-4daa-9d9b-2efaeabdbaac","year":2021},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.496694Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:df74e8915b2ee9de99a3fc73df390124e048806e404d717b3fa06ee7e5c6d5b6","observation_id":"4ed95b76-f470-49fa-8c21-9a79c8bd3e70","resolution":{"observed_at":"2026-08-05T05:33:48.776124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.756597Z","title":"Point transformer v2: Grouped vector attention and partition-based pooling","venue":null,"work_id":"6995cff0-121c-4430-a6ab-e1b98b678f9f","year":2022},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.500552Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:4eddb3fdedbfbd3b6e5c4b4d6736b25b11dc481a4712566b621be47b5018c886","observation_id":"6dcea78c-27f1-4914-9be6-820522fb9734","resolution":{"observed_at":"2026-08-05T05:33:48.761392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.741047Z","title":"Point transformer v3: Simpler faster stronger","venue":null,"work_id":"96e6ba1c-a649-4d2f-bbdf-e66012cf72fb","year":2024},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.505169Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:484b939aa67e88e4242056970823a7395cd1ed45a12d9ef9eb44c1f32e1f1c2b","observation_id":"62ee0c84-0875-43d7-958f-b2adb7af29c5","resolution":{"observed_at":"2026-08-05T05:33:48.745548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.727145Z","title":"Pct: Point cloud transformer","venue":null,"work_id":"d6bd0d0a-9c87-40ea-a0af-f1d1571c3f4d","year":2021},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.509452Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:ca37615384f082e09eefee7d469d6e726962374c46d1cc5d42fef3dc94c050f1","observation_id":"b3b4004b-652d-4412-9a8b-ebc2ff2b2930","resolution":{"observed_at":"2026-08-05T05:33:48.731276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08134","last_updated":"2023-05-10T15:29:07Z","snapshot_observed_at":"2026-08-13T12:24:41.305268Z","submitted_at":"2023-03-14T17:59:02Z","title":"Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08134","snapshot_observed_at":"2026-08-05T05:33:48.513954Z","title":"Parameter is not all you need: Starting from non-parametric networks for 3d point cloud analysis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.513954Z"},"links":{"cited_paper":"/paper/2303.08134","citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:d89d78d15f120adbd7b45245c7288e2aa126b542b7c82ac61d329aae7ff086cf","observation_id":"62f85dda-1ba7-4af6-9d0d-106e475a0d33","resolution":{"observed_at":"2026-08-05T05:33:48.513954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.03056","last_updated":"2024-12-07T05:07:59Z","snapshot_observed_at":"2026-08-14T09:05:55.397949Z","submitted_at":"2024-12-04T06:20:51Z","title":"Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification","version":2},"cited_work":{"arxiv_id":"2412.03056","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.03056","snapshot_observed_at":"2026-08-05T05:33:48.567874Z","title":"Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification","venue":"cs.CV","work_id":"ce75bf1d-b98f-4697-9043-7d3f01ae678e","year":2024},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.518590Z"},"links":{"cited_paper":"/paper/2412.03056","citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:a2e4ef3b134b985d91286495050b5051a6339ea89f6165261cd909e3df3e868c","observation_id":"3d3a5fac-f9ef-452e-988c-5ad3ea74992f","resolution":{"observed_at":"2026-08-05T05:33:48.574721Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.713106Z","title":"Point-ln: A lightweight framework for efficient point cloud classification using non-parametric positional encoding","venue":null,"work_id":"e8942e80-1cc8-4047-be29-91ce3801706d","year":2025},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.523029Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:d6b9ad60a3c266ebd93507db3f18cb0663edba636a01977e2d2ef4c811f3c2e9","observation_id":"caa89582-f754-4898-a2e3-a3a25d086271","resolution":{"observed_at":"2026-08-05T05:33:48.717537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.698664Z","title":"Point-planenet: Plane kernel based con- volutional neural network for point clouds analysis","venue":null,"work_id":"6466f019-3e6c-4a6d-a8c0-ec8267392692","year":2020},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.527102Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:efb342fc490692c4c13765e18d6aa6452c7f024f98e059527ba17a84ad50763d","observation_id":"06807e13-3b76-4939-8ab5-8f706a5f2c58","resolution":{"observed_at":"2026-08-05T05:33:48.703040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T05:33:48.681223Z","title":"Walk in the cloud: Learning curves for point clouds shape analysis","venue":null,"work_id":"8838d4c4-017a-400c-87ad-0dab1c10b1c6","year":2021},"citing_paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T05:33:48.531558Z"},"links":{"citing_paper":"/paper/2509.05198"},"observation_digest":"sha256:5acafc85f588a2f4347a9cd1110fe7ab36c749cd6957d388def8a902ce27faf6","observation_id":"d50b0cb4-0f9c-4111-892e-b20a25bab557","resolution":{"observed_at":"2026-08-05T05:33:48.687367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.05198","last_updated":"2025-09-05T15:57:36Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T06:34:24.848178Z","submitted_at":"2025-09-05T15:57:36Z","title":"Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":33},"total_outbound_references":41},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2509.05198."}