{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QQJFENZUOR435YXMONZU57BKOI","short_pith_number":"pith:QQJFENZU","schema_version":"1.0","canonical_sha256":"84125237347479bee2ec73734efc2a7200721f8e87ba5222ba87dbc018d7e7f9","source":{"kind":"arxiv","id":"1905.13445","version":1},"attestation_state":"computed","paper":{"title":"Point Clouds Learning with Attention-based Graph Convolution Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Peng, Junzhou Chen, Zhuyang Xie","submitted_at":"2019-05-31T07:10:12Z","abstract_excerpt":"Point clouds data, as one kind of representation of 3D objects, are the most primitive output obtained by 3D sensors. Unlike 2D images, point clouds are disordered and unstructured. Hence it is not straightforward to apply classification techniques such as the convolution neural network to point clouds analysis directly. To solve this problem, we propose a novel network structure, named Attention-based Graph Convolution Networks (AGCN), to extract point clouds features. Taking the learning process as a message propagation between adjacent points, we introduce an attention mechanism to AGCN for"},"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":"1905.13445","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-31T07:10:12Z","cross_cats_sorted":[],"title_canon_sha256":"3f08831bd76bf8c1ebc3ea110ef9b138566786a6211c2b4739f809d9b3074e65","abstract_canon_sha256":"9c8dc985647dbe635b54957be2297871a56ef812d12665a6fb02690d31edef40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:34.902347Z","signature_b64":"6VQrAijK+dRtxmimNyitWhtRzCrM5TbMthKpAqWYkhhj6Kw5qDAuZ3+6rD4ZJAE8LyH7/nRrZoS0/DkF6Id1Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84125237347479bee2ec73734efc2a7200721f8e87ba5222ba87dbc018d7e7f9","last_reissued_at":"2026-05-17T23:44:34.901786Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:34.901786Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Point Clouds Learning with Attention-based Graph Convolution Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Peng, Junzhou Chen, Zhuyang Xie","submitted_at":"2019-05-31T07:10:12Z","abstract_excerpt":"Point clouds data, as one kind of representation of 3D objects, are the most primitive output obtained by 3D sensors. Unlike 2D images, point clouds are disordered and unstructured. Hence it is not straightforward to apply classification techniques such as the convolution neural network to point clouds analysis directly. To solve this problem, we propose a novel network structure, named Attention-based Graph Convolution Networks (AGCN), to extract point clouds features. Taking the learning process as a message propagation between adjacent points, we introduce an attention mechanism to AGCN for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.13445","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1905.13445","created_at":"2026-05-17T23:44:34.901866+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.13445v1","created_at":"2026-05-17T23:44:34.901866+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.13445","created_at":"2026-05-17T23:44:34.901866+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQJFENZUOR43","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQJFENZUOR435YXM","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQJFENZU","created_at":"2026-05-18T12:33:27.125529+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI","json":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI.json","graph_json":"https://pith.science/api/pith-number/QQJFENZUOR435YXMONZU57BKOI/graph.json","events_json":"https://pith.science/api/pith-number/QQJFENZUOR435YXMONZU57BKOI/events.json","paper":"https://pith.science/paper/QQJFENZU"},"agent_actions":{"view_html":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI","download_json":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI.json","view_paper":"https://pith.science/paper/QQJFENZU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.13445&json=true","fetch_graph":"https://pith.science/api/pith-number/QQJFENZUOR435YXMONZU57BKOI/graph.json","fetch_events":"https://pith.science/api/pith-number/QQJFENZUOR435YXMONZU57BKOI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI/action/storage_attestation","attest_author":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI/action/author_attestation","sign_citation":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI/action/citation_signature","submit_replication":"https://pith.science/pith/QQJFENZUOR435YXMONZU57BKOI/action/replication_record"}},"created_at":"2026-05-17T23:44:34.901866+00:00","updated_at":"2026-05-17T23:44:34.901866+00:00"}