{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SSO5OOCDUDJ73N2PESHYERYBLA","short_pith_number":"pith:SSO5OOCD","schema_version":"1.0","canonical_sha256":"949dd73843a0d3fdb74f248f8247015806c79441f270a5a286ec98d22daec355","source":{"kind":"arxiv","id":"1904.10014","version":2},"attestation_state":"computed","paper":{"title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenglong Fu, Clarence W. de Silva, Jing Wang, Kuangen Zhang, Ming Hao","submitted_at":"2019-04-22T18:16:34Z","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1904.10014","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-04-22T18:16:34Z","cross_cats_sorted":[],"title_canon_sha256":"967edf89f0f51ad869759d4e1dad4054c201ab8641b14f4a51e9e36f547234a4","abstract_canon_sha256":"c61309ba82fbe5cb4e000ab4a3a4a2c233b1ae5126170783f9c5ba24f3c3401a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:51:41.845375Z","signature_b64":"54ukDsnp/Uti04cjqLJRLxAdhjibPNJhouETCVR4SAnnNEy1e5gxymlQoLXT19Og6pp06w5whGFB51VN2tPjAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"949dd73843a0d3fdb74f248f8247015806c79441f270a5a286ec98d22daec355","last_reissued_at":"2026-07-04T23:51:41.844929Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:51:41.844929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenglong Fu, Clarence W. de Silva, Jing Wang, Kuangen Zhang, Ming Hao","submitted_at":"2019-04-22T18:16:34Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.10014","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1904.10014/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.10014","created_at":"2026-07-04T23:51:41.844993+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.10014v2","created_at":"2026-07-04T23:51:41.844993+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.10014","created_at":"2026-07-04T23:51:41.844993+00:00"},{"alias_kind":"pith_short_12","alias_value":"SSO5OOCDUDJ7","created_at":"2026-07-04T23:51:41.844993+00:00"},{"alias_kind":"pith_short_16","alias_value":"SSO5OOCDUDJ73N2P","created_at":"2026-07-04T23:51:41.844993+00:00"},{"alias_kind":"pith_short_8","alias_value":"SSO5OOCD","created_at":"2026-07-04T23:51:41.844993+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06864","citing_title":"Face recognition on point cloud with cgan-top for denoising","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA","json":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA.json","graph_json":"https://pith.science/api/pith-number/SSO5OOCDUDJ73N2PESHYERYBLA/graph.json","events_json":"https://pith.science/api/pith-number/SSO5OOCDUDJ73N2PESHYERYBLA/events.json","paper":"https://pith.science/paper/SSO5OOCD"},"agent_actions":{"view_html":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA","download_json":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA.json","view_paper":"https://pith.science/paper/SSO5OOCD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.10014&json=true","fetch_graph":"https://pith.science/api/pith-number/SSO5OOCDUDJ73N2PESHYERYBLA/graph.json","fetch_events":"https://pith.science/api/pith-number/SSO5OOCDUDJ73N2PESHYERYBLA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA/action/storage_attestation","attest_author":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA/action/author_attestation","sign_citation":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA/action/citation_signature","submit_replication":"https://pith.science/pith/SSO5OOCDUDJ73N2PESHYERYBLA/action/replication_record"}},"created_at":"2026-07-04T23:51:41.844993+00:00","updated_at":"2026-07-04T23:51:41.844993+00:00"}