{"as_of":"2026-08-09T17:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c796d23bcb832f5ad933c0b6d562bd17dbc025f7291d25c55940003ea1301ef0","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:10:33.020056Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2507.22020/citation-record","integrity":"/paper/2507.22020/integrity","json":"/paper/2507.22020/citation-record.json","paper":"/paper/2507.22020"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.33701","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:10:34.078911Z","title":"Explainable artificial intelligence for machine learning-based photogrammetric point cloud classification","venue":null,"work_id":"500609fe-0f39-4b6b-9b3c-6d9941d61c27","year":2024},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.901703Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:9a7fcc0717ff9c1cb83fa302dae1f12eabb28c72dadb389c757b38c0caa8f888","observation_id":"c9a21ca2-c25b-4c3a-a3b9-b5ee9857c7ea","resolution":{"observed_at":"2026-08-06T12:10:34.086315Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/s22041357","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:10:33.218225Z","title":"Recent advancements in learning algorithms for point clouds: An updated overview","venue":null,"work_id":"ec8117ab-6410-40f2-8332-78769a3967c2","year":2022},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.906030Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:66fa8bb1b7a2fce59ee7d294180a08168151b82bc1a40f2eda732db6a95218b8","observation_id":"ce806381-f5fc-431e-906b-a20bce6cfc0a","resolution":{"observed_at":"2026-08-06T12:10:33.223146Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-07T11:28:03.211074Z","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":"2026-08-06T12:10:32.910123Z","title":"Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.910123Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:263a8f27cd61f8f35e072cbf77e49ea30a5f167b775f32a49d46fbcf02cf3bfa","observation_id":"ee2989fa-0496-4b97-a6ec-cdcecf85824d","resolution":{"observed_at":"2026-08-06T12:10:32.910123Z","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":"2026-08-06T12:10:34.174430Z","title":"A density-based algorithm for discovering clusters in large spatial databases with noise","venue":null,"work_id":"35351b29-f8f0-4919-b8d0-9715a740a5fb","year":1996},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.914263Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:dd25e1ae00dcf20f277278689ae26f4a24cdc4d7e23e2a1deea1829c48ac6bf2","observation_id":"7fe74bc2-52b9-4684-973e-44a301ce694b","resolution":{"observed_at":"2026-08-06T12:10:34.181548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:34.159032Z","title":"Deep learning for 3D point clouds: A survey","venue":null,"work_id":"79613e3d-8f1c-4712-90b0-86c30a4088b9","year":null},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.918480Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:a9921c5fd026840384924f70fb5ffe2b02bdde91dfbc406e6c6e5777e9b8ae89","observation_id":"e6352bf8-caa0-4969-9c57-75fa5540a947","resolution":{"observed_at":"2026-08-06T12:10:34.163877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-46452-2_27","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:03:56.115653Z","title":"Toward Explainable Metrology 4.0: Utilizing Explainable AI to Predict the Pointwise Accuracy of Laser Scanning Devices in Industrial Manufacturing, pages 479–501","venue":null,"work_id":"d740a084-cc7f-4ee8-a862-45704ca07ce5","year":2024},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.926405Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:0bb978d23ce8373851e83b0f8bfb707c64cb9cf0af91e433af65ec3b579d3545","observation_id":"4e5a2d09-2407-4b6c-974c-04351ad1785d","resolution":{"observed_at":"2026-08-06T12:10:33.208436Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:34.143261Z","title":"A unified approach to interpreting model predictions","venue":null,"work_id":"b64cabd4-40af-46c9-9be9-0727794c832a","year":2017},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.930652Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:7b1f6ed98a6baf41a8e2e298ee321aa58317566a57773b0fa500ad6ecfd56150","observation_id":"ef19b28c-527c-4954-a8cb-07e7d2de424f","resolution":{"observed_at":"2026-08-06T12:10:34.148228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:34.128203Z","title":"Some methods for classification and analysis of multivariate observations","venue":null,"work_id":"490d8490-baea-42af-94f1-8bcf031b042c","year":1967},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.934433Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:ba02d91ba959129d83d44ce573eaf4eb8a3e44772393b99b48cc65005962040c","observation_id":"38ce0768-f580-45aa-882b-66943a2ed53d","resolution":{"observed_at":"2026-08-06T12:10:34.133008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.31952","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:10:33.892177Z","title":"BubblEX: An explainable deep learning framework for point-cloud classification","venue":null,"work_id":"015c7497-2514-4e77-b661-ad5833cac276","year":2022},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.938480Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:f85e1dfdf152cd78d428533d072cfd234e67f1e27e73ff8a09f8119db0298fcd","observation_id":"c9c87bb2-02dc-4097-85d8-fefb1f3a3cc3","resolution":{"observed_at":"2026-08-06T12:10:33.899485Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.16966","last_updated":"2023-03-02T17:21:16Z","snapshot_observed_at":"2026-08-04T14:15:16.424745Z","submitted_at":"2022-10-30T22:24:43Z","title":"Interpretable Geometric Deep Learning via Learnable Randomness Injection","version":2},"cited_work":{"arxiv_id":"2210.16966","doi":"10.48550/arxiv.2210.16966","metadata_source":"pith","pith_arxiv_id":"2210.16966","snapshot_observed_at":"2026-08-06T12:16:18.182151Z","title":"Interpretable Geometric Deep Learning via Learnable Randomness Injection","venue":"cs.LG","work_id":"063ca067-95d8-4720-8a91-b24eed4d6ece","year":2022},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.942481Z"},"links":{"cited_paper":"/paper/2210.16966","citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:164f0bad8fbf07ec493fb3933a299f603aded554125dac29070c87f6ca1f9380","observation_id":"829842eb-083b-49fc-be33-1df5a9526f74","resolution":{"observed_at":"2026-08-06T12:10:33.193716Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:32.946999Z","title":"Explainable artificial intelligence (xai) for methods working on point cloud data: A survey","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.946999Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:fd67adbbacdd3ecfc81d5b1fad0385d651f71359182202876d73ed015c5e6825","observation_id":"f4a87431-9099-480b-b4e8-b75397f0cc6f","resolution":{"observed_at":"2026-08-06T12:10:32.946999Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-06T12:10:32.951228Z","title":"Qi, Hao Su, Kaichun Mo, and Leonidas J","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.951228Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:fcbb9caa2e9f2d0fb9919542e98fe42a0ec05d49a86041e697e6d706d6c6b543","observation_id":"e3b12857-76a8-416e-9f40-8f28bdcf82e2","resolution":{"observed_at":"2026-08-06T12:10:32.951228Z","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":"2026-08-06T12:10:32.955294Z","title":"Why Should I Trust You?","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.955294Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:76c5a54ace16b4f55329212da224cfac9b3cd9c73151dd968ed9bac2cabeb228","observation_id":"0e887e33-9c67-43a8-8f0a-f8d544d1687c","resolution":{"observed_at":"2026-08-06T12:10:32.955294Z","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":"10.1002/widm.1554","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:03:56.115653Z","title":"From 3d point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges","venue":"Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery","work_id":"b12c1942-cb96-4df5-b6d5-5a556ce62a2b","year":2024},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.961171Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:189b1cfbeb14b3c93108303f662d3f1e495d24a98fe221a8b8654b29fb241f81","observation_id":"a65ee553-180b-4215-8ae4-c644ef4bdd59","resolution":{"observed_at":"2026-08-06T12:10:33.163991Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:32.965255Z","title":null,"venue":null,"work_id":null,"year":1953},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.965255Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:9a51f8f5a289b7decf8862d9ce07f4a2fae7fb7ba3c479d3a65196041f3c6a94","observation_id":"ac9c96cb-1b7a-4c72-97dc-21a8bc7cf9dd","resolution":{"observed_at":"2026-08-06T12:10:32.965255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.03549","last_updated":"2021-11-05T15:14:34Z","snapshot_observed_at":"2026-08-09T00:35:38.479462Z","submitted_at":"2021-11-05T15:14:34Z","title":"Interpreting Representation Quality of DNNs for 3D Point Cloud Processing","version":1},"cited_work":{"arxiv_id":"2111.03549","doi":"10.48550/arxiv.2111.03549","metadata_source":"pith","pith_arxiv_id":"2111.03549","snapshot_observed_at":"2026-08-06T12:16:18.182151Z","title":"Interpreting Representation Quality of DNNs for 3D Point Cloud Processing","venue":"cs.CV","work_id":"9d024435-e17e-4c47-acf0-9e54882b1bf4","year":2021},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.969478Z"},"links":{"cited_paper":"/paper/2111.03549","citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:45bc4383d86c82ec3d2d89ccced777eb0d6ad5ceb32a2f4596c28334ae4905ef","observation_id":"bed968d3-bc13-4955-957e-36d8d691cd3c","resolution":{"observed_at":"2026-08-06T12:10:33.140023Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:32.973688Z","title":"Interpretable rotation-equivariant quaternion neural networks for 3D point cloud processing","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.973688Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:dc552de24a9fab6355beaf046b681cfd939263c47bdd8c1390ced1e9ee890ce8","observation_id":"7faaa39b-f496-4f8b-8eb8-873cbde57c6c","resolution":{"observed_at":"2026-08-06T12:10:32.973688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.01365","last_updated":"2017-06-13T01:52:38Z","snapshot_observed_at":"2026-07-06T05:32:18.507883Z","submitted_at":"2017-03-04T00:18:49Z","title":"Axiomatic Attribution for Deep Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.01365","snapshot_observed_at":"2026-08-06T12:10:32.977527Z","title":"Axiomatic attribution for deep networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.977527Z"},"links":{"cited_paper":"/paper/1703.01365","citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:0e54205670cdf45dc5d87186c2e99db2a21df8f9122afd11833ae60b6c618bad","observation_id":"7de3c3ec-1f98-468d-912e-9a7831c2f5ba","resolution":{"observed_at":"2026-08-06T12:10:32.977527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.04525","last_updated":"2020-07-09T03:06:06Z","snapshot_observed_at":"2026-08-07T18:33:12.642125Z","submitted_at":"2020-07-09T03:06:06Z","title":"PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing","version":1},"cited_work":{"arxiv_id":"2007.04525","doi":"10.48550/arxiv.2007.04525","metadata_source":"pith","pith_arxiv_id":"2007.04525","snapshot_observed_at":"2026-08-06T12:16:18.182151Z","title":"PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing","venue":"cs.CV","work_id":"1e26eaa8-c688-4e99-80ee-c45527e144a1","year":2020},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.982233Z"},"links":{"cited_paper":"/paper/2007.04525","citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:70126b78c1cdbff37931942e1e9c1d5f0c69f19c658524f004aa7d5561c0df54","observation_id":"c856750b-14b7-4a79-9be7-d69e80fc75fd","resolution":{"observed_at":"2026-08-06T12:10:33.105115Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18760","last_updated":"2024-04-29T14:57:16Z","snapshot_observed_at":"2026-08-06T15:25:02.409428Z","submitted_at":"2024-04-29T14:57:16Z","title":"Flow AM: Generating Point Cloud Global Explanations by Latent Alignment","version":1},"cited_work":{"arxiv_id":"2404.18760","doi":"10.48550/arxiv.2404.18760","metadata_source":"pith","pith_arxiv_id":"2404.18760","snapshot_observed_at":"2026-08-06T12:16:18.182151Z","title":"Flow AM: Generating Point Cloud Global Explanations by Latent Alignment","venue":"cs.CV","work_id":"47314816-0d73-4c36-93d0-29ff463ff2b7","year":2024},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.995027Z"},"links":{"cited_paper":"/paper/2404.18760","citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:8dfe103fea39fc06cf5d21d764b430334654eff4d4e33c731bedb861fef27e41","observation_id":"f8199187-5dcc-41b3-ad65-dbe387e18114","resolution":{"observed_at":"2026-08-06T12:10:33.084132Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:32.999306Z","title":"Surrogate model-based explainability methods for point cloud NNs","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.999306Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:5889ba181c31b49b42405ccd9aec020f2ef81dd03a6be413e75229d3c4f2304f","observation_id":"bf58e53c-6387-409b-a846-d3c5842a0c87","resolution":{"observed_at":"2026-08-06T12:10:32.999306Z","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":"2026-08-06T12:10:33.003413Z","title":"Explainability-aware one point attack for point cloud neural networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:33.003413Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:960bdb1d28c45346b217606001a52ed182591ab46adffb4bc6587de204747d28","observation_id":"5e72f3b2-7d77-4a91-b179-37df40a0825e","resolution":{"observed_at":"2026-08-06T12:10:33.003413Z","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":"2026-08-06T12:10:34.112579Z","title":null,"venue":null,"work_id":"ccbfa1d5-0d09-453d-9f41-00095db1375c","year":2022},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:33.007629Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:5497f890d19e2f57e885e3510293172918e3cd9ffca10d4b466022a91fcbe812","observation_id":"6b67b7d5-6dd7-45dc-ac64-c91569d72b98","resolution":{"observed_at":"2026-08-06T12:10:34.117146Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:34.097490Z","title":"A scalable active framework for region annotation in 3d shape collections","venue":null,"work_id":"5dc0f277-cf45-420a-ac57-96127327e295","year":2016},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:33.011701Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:24e37ca98ebd6132d60664781c367f29ed42d937f9288076e0e0ba572724d429","observation_id":"c16a6b77-cf84-4453-bd7a-a0a4464eba94","resolution":{"observed_at":"2026-08-06T12:10:34.102569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29635","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:10:33.425172Z","title":null,"venue":null,"work_id":"5ec0a04a-ed13-4568-8bb3-eb3fb22a416d","year":2020},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:33.015753Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:4d91fea517fe298c17d5eb467992b1e3847a2649ff46c68793cf0d7851e19bfa","observation_id":"3bf98861-7366-4399-9a38-ae5c36002091","resolution":{"observed_at":"2026-08-06T12:10:33.432259Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.01687","last_updated":"2019-09-12T19:29:12Z","snapshot_observed_at":"2026-08-07T05:45:46.914128Z","submitted_at":"2018-11-28T21:50:49Z","title":"PointCloud Saliency Maps","version":6},"cited_work":{"arxiv_id":"1812.01687","doi":"10.48550/arxiv.1812.01687","metadata_source":"pith","pith_arxiv_id":"1812.01687","snapshot_observed_at":"2026-08-06T12:16:18.182151Z","title":"PointCloud Saliency Maps","venue":"cs.CV","work_id":"daca404f-760f-42e2-b933-832a1b8869f3","year":2018},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:33.020056Z"},"links":{"cited_paper":"/paper/1812.01687","citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:0ad511adf882038bf214355d2f9e467acb7b900fadfbb12c0ad1758f7a58cde8","observation_id":"346ff47a-bbd5-4560-9b13-2f5183bdea43","resolution":{"observed_at":"2026-08-06T12:10:33.062154Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T12:10:32.922383Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:32.922383Z"},"links":{"citing_paper":"/paper/2507.22020"},"observation_digest":"sha256:84b7b4e01e2310191e4a7995833270cb051dc1d30c50b5e6fd61f309b14c6df7","observation_id":"6aa38f46-2c29-4be9-a9a9-5c0589aca940","resolution":{"observed_at":"2026-08-06T12:10:32.922383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.22020","last_updated":"2025-07-29T17:12:16Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T15:49:05.774921Z","submitted_at":"2025-07-29T17:12:16Z","title":"XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":1,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":10,"verified_exact":8,"verified_fuzzy":5},"total_outbound_references":27},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.22020."}