{"as_of":"2026-08-12T03:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fa9f48bf81434ce24985cc43a752cdc67c25e728b64ad2edffbc13440872b7a9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T04:32:10.762647Z","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":"2026-08-07T05:50:36.352462Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1904.10014","last_updated":"2019-08-06T03:56:20Z","snapshot_observed_at":"2026-07-06T07:47:38.383855Z","submitted_at":"2019-04-22T18:16:34Z","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.10014","snapshot_observed_at":"2026-08-11T04:32:10.762647Z","title":"PU-GAN: A Point Cloud Upsam- pling Adversarial Network,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.18775","last_updated":"2024-12-25T04:34:22Z","snapshot_observed_at":"2026-08-11T04:26:46.323911Z","submitted_at":"2024-12-25T04:34:22Z","title":"ObitoNet: Multimodal High-Resolution Point Cloud Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T04:32:10.762647Z"},"links":{"cited_paper":"/paper/1904.10014","citing_paper":"/paper/2412.18775"},"observation_digest":"sha256:1d700949fecf57e2cb8fd1583eea4befef417523e7c997d06cd8863dbb405f65","observation_id":"f1f31ad6-5dc6-4b22-a662-08754bcc5bfe","resolution":{"observed_at":"2026-08-11T04:32:10.762647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.10014","last_updated":"2019-08-06T03:56:20Z","snapshot_observed_at":"2026-07-06T07:47:38.383855Z","submitted_at":"2019-04-22T18:16:34Z","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.10014","snapshot_observed_at":"2026-08-10T16:57:29.397456Z","title":"Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":119,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.397456Z"},"links":{"cited_paper":"/paper/1904.10014","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:84498f9042d6ef7d4fdf82122b371ff94085fd05892c9d5fb0448dd12f8733e9","observation_id":"aa9b26f0-a9b7-4bbe-8e75-ed52a49fb17b","resolution":{"observed_at":"2026-08-10T16:57:29.397456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.10014","last_updated":"2019-08-06T03:56:20Z","snapshot_observed_at":"2026-07-06T07:47:38.383855Z","submitted_at":"2019-04-22T18:16:34Z","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.10014","snapshot_observed_at":"2026-08-10T14:30:48.478533Z","title":"Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.15286","last_updated":"2025-01-25T17:50:53Z","snapshot_observed_at":"2026-08-10T14:23:45.744247Z","submitted_at":"2025-01-25T17:50:53Z","title":"Efficient Point Clouds Upsampling via Flow Matching","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T14:30:48.478533Z"},"links":{"cited_paper":"/paper/1904.10014","citing_paper":"/paper/2501.15286"},"observation_digest":"sha256:0ad714e5aa98d7aa2238bbe65baafe800bf9f1ffa88cb5f1e91785149e364a9f","observation_id":"3b8a9995-702f-485d-a1f3-00030f03c057","resolution":{"observed_at":"2026-08-10T14:30:48.478533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.10014","last_updated":"2019-08-06T03:56:20Z","snapshot_observed_at":"2026-07-06T07:47:38.383855Z","submitted_at":"2019-04-22T18:16:34Z","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","version":2},"cited_work":{"arxiv_id":"1904.10014","doi":null,"metadata_source":"pith","pith_arxiv_id":"1904.10014","snapshot_observed_at":"2026-08-07T05:50:36.352462Z","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","venue":"cs.CV","work_id":"080536a6-b6f8-417b-b5ca-b4cd61677e1d","year":2019},"citing_paper":{"arxiv_id":"2506.06864","last_updated":"2025-06-07T17:09:31Z","snapshot_observed_at":"2026-08-10T11:58:15.182268Z","submitted_at":"2025-06-07T17:09:31Z","title":"Face recognition on point cloud with cgan-top for denoising","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:50:33.909584Z"},"links":{"cited_paper":"/paper/1904.10014","citing_paper":"/paper/2506.06864"},"observation_digest":"sha256:2dbb34f84dda62194a3344ee5f0b862dd2165f82a5123fdd0f7aa04e67dd62c9","observation_id":"678f08b9-49ae-4416-b0fe-3b6f8560c58a","resolution":{"observed_at":"2026-08-07T05:50:36.395375Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1904.10014/citation-record","integrity":"/paper/1904.10014/integrity","json":"/paper/1904.10014/citation-record.json","paper":"/paper/1904.10014"},"outbound":[],"paper":{"arxiv_id":"1904.10014","last_updated":"2019-08-06T03:56:20Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T07:47:38.383855Z","submitted_at":"2019-04-22T18:16:34Z","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1904.10014."}