{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:SSO5OOCDUDJ73N2PESHYERYBLA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c61309ba82fbe5cb4e000ab4a3a4a2c233b1ae5126170783f9c5ba24f3c3401a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-22T18:16:34Z","title_canon_sha256":"967edf89f0f51ad869759d4e1dad4054c201ab8641b14f4a51e9e36f547234a4"},"schema_version":"1.0","source":{"id":"1904.10014","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.10014","created_at":"2026-07-04T23:51:41Z"},{"alias_kind":"arxiv_version","alias_value":"1904.10014v2","created_at":"2026-07-04T23:51:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.10014","created_at":"2026-07-04T23:51:41Z"},{"alias_kind":"pith_short_12","alias_value":"SSO5OOCDUDJ7","created_at":"2026-07-04T23:51:41Z"},{"alias_kind":"pith_short_16","alias_value":"SSO5OOCDUDJ73N2P","created_at":"2026-07-04T23:51:41Z"},{"alias_kind":"pith_short_8","alias_value":"SSO5OOCD","created_at":"2026-07-04T23:51:41Z"}],"graph_snapshots":[{"event_id":"sha256:0d6610383b7fb36abd0abffca2b5b22f1ec7117c7f6edb98fa36b33c34aeb1a0","target":"graph","created_at":"2026-07-04T23:51:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1904.10014/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly. However, the point cloud is sparse, unstructured, and unordered, which cannot be recognized accurately by a traditional convolutional neural network (CNN) nor a recurrent neural network (RNN). Fortunately, a graph convolutional neural network (Graph CNN) can process sparse and unordered data. Hence, we propose a linked dynamic graph CNN (LDGCNN) to classify and segment point cloud directly in this paper. We remove the transformation net","authors_text":"Chenglong Fu, Clarence W. de Silva, Jing Wang, Kuangen Zhang, Ming Hao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-22T18:16:34Z","title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.10014","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b90162cc1542cc0ef5a912b0c8f96a190f3bfc6ff65abd5e514dff404aa8e757","target":"record","created_at":"2026-07-04T23:51:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c61309ba82fbe5cb4e000ab4a3a4a2c233b1ae5126170783f9c5ba24f3c3401a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-22T18:16:34Z","title_canon_sha256":"967edf89f0f51ad869759d4e1dad4054c201ab8641b14f4a51e9e36f547234a4"},"schema_version":"1.0","source":{"id":"1904.10014","kind":"arxiv","version":2}},"canonical_sha256":"949dd73843a0d3fdb74f248f8247015806c79441f270a5a286ec98d22daec355","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"949dd73843a0d3fdb74f248f8247015806c79441f270a5a286ec98d22daec355","first_computed_at":"2026-07-04T23:51:41.844929Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:51:41.844929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"54ukDsnp/Uti04cjqLJRLxAdhjibPNJhouETCVR4SAnnNEy1e5gxymlQoLXT19Og6pp06w5whGFB51VN2tPjAg==","signature_status":"signed_v1","signed_at":"2026-07-04T23:51:41.845375Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.10014","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b90162cc1542cc0ef5a912b0c8f96a190f3bfc6ff65abd5e514dff404aa8e757","sha256:0d6610383b7fb36abd0abffca2b5b22f1ec7117c7f6edb98fa36b33c34aeb1a0"],"state_sha256":"d147a116d17de461e2003d7dd28bebad28e5dec68b24d57171968c8fd9a44742"}