{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:T3IW44PSF3ZCWJ3AKDB3POFN7L","short_pith_number":"pith:T3IW44PS","canonical_record":{"source":{"id":"1811.02565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-06T15:07:03Z","cross_cats_sorted":[],"title_canon_sha256":"bd876ef34dbcb337ea68e8b4eaa9c7e94c9ead7b0f63f9cef770a6ca643b8656","abstract_canon_sha256":"d25c56fdf15dce6907feb9d87a40f386e0720e2132fa604e28d2620dde456e85"},"schema_version":"1.0"},"canonical_sha256":"9ed16e71f22ef22b276050c3b7b8adfaf95f6bf52e47d5b1fb1020b912b82bc5","source":{"kind":"arxiv","id":"1811.02565","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02565","created_at":"2026-05-18T00:00:39Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02565v2","created_at":"2026-05-18T00:00:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02565","created_at":"2026-05-18T00:00:39Z"},{"alias_kind":"pith_short_12","alias_value":"T3IW44PSF3ZC","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"T3IW44PSF3ZCWJ3A","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"T3IW44PS","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:T3IW44PSF3ZCWJ3AKDB3POFN7L","target":"record","payload":{"canonical_record":{"source":{"id":"1811.02565","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-06T15:07:03Z","cross_cats_sorted":[],"title_canon_sha256":"bd876ef34dbcb337ea68e8b4eaa9c7e94c9ead7b0f63f9cef770a6ca643b8656","abstract_canon_sha256":"d25c56fdf15dce6907feb9d87a40f386e0720e2132fa604e28d2620dde456e85"},"schema_version":"1.0"},"canonical_sha256":"9ed16e71f22ef22b276050c3b7b8adfaf95f6bf52e47d5b1fb1020b912b82bc5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:39.086079Z","signature_b64":"jcazieXBrNnv0vyVE8TvyXgF0TGR0y57Sz60F6p+xvj/uDg0p2/A2leLPPCbVN5y12cWlVgAKeDEjbgaJgPhAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ed16e71f22ef22b276050c3b7b8adfaf95f6bf52e47d5b1fb1020b912b82bc5","last_reissued_at":"2026-05-18T00:00:39.085629Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:39.085629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.02565","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"87RMkYPSWqaUQWgXjtVDLUkIr9A5xsRTEQJFJh7sOAXuObgg6XcD+5G5lS/CzFD2lg9yyzDLOd88yclYRiQtCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:51:28.835937Z"},"content_sha256":"57c81ef6a40925ab3a10ef98fc50c5b981e843ff4ebf9ddcac66e8f1dbacb957","schema_version":"1.0","event_id":"sha256:57c81ef6a40925ab3a10ef98fc50c5b981e843ff4ebf9ddcac66e8f1dbacb957"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:T3IW44PSF3ZCWJ3AKDB3POFN7L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Matthias Zwicker, Xinhai Liu, Yu-Shen Liu, Zhizhong Han","submitted_at":"2018-11-06T15:07:03Z","abstract_excerpt":"Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-gra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02565","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:00:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pJ/f8/+QSPf6r4ZzGTa6DppsgrfDGHs96XEXcFVgjPWwllRiDdY4XLGOyJYiwxTmY4wd7eqKBYJKGWCTLNBBCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:51:28.836397Z"},"content_sha256":"7623386e93f55e7c8d2a8f82b88a2c20ba54c1c42d8730de5e9112d4ab980422","schema_version":"1.0","event_id":"sha256:7623386e93f55e7c8d2a8f82b88a2c20ba54c1c42d8730de5e9112d4ab980422"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T3IW44PSF3ZCWJ3AKDB3POFN7L/bundle.json","state_url":"https://pith.science/pith/T3IW44PSF3ZCWJ3AKDB3POFN7L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T3IW44PSF3ZCWJ3AKDB3POFN7L/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T07:51:28Z","links":{"resolver":"https://pith.science/pith/T3IW44PSF3ZCWJ3AKDB3POFN7L","bundle":"https://pith.science/pith/T3IW44PSF3ZCWJ3AKDB3POFN7L/bundle.json","state":"https://pith.science/pith/T3IW44PSF3ZCWJ3AKDB3POFN7L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T3IW44PSF3ZCWJ3AKDB3POFN7L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:T3IW44PSF3ZCWJ3AKDB3POFN7L","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":"d25c56fdf15dce6907feb9d87a40f386e0720e2132fa604e28d2620dde456e85","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-06T15:07:03Z","title_canon_sha256":"bd876ef34dbcb337ea68e8b4eaa9c7e94c9ead7b0f63f9cef770a6ca643b8656"},"schema_version":"1.0","source":{"id":"1811.02565","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02565","created_at":"2026-05-18T00:00:39Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02565v2","created_at":"2026-05-18T00:00:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02565","created_at":"2026-05-18T00:00:39Z"},{"alias_kind":"pith_short_12","alias_value":"T3IW44PSF3ZC","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"T3IW44PSF3ZCWJ3A","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"T3IW44PS","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:7623386e93f55e7c8d2a8f82b88a2c20ba54c1c42d8730de5e9112d4ab980422","target":"graph","created_at":"2026-05-18T00:00:39Z","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"},"paper":{"abstract_excerpt":"Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-gra","authors_text":"Matthias Zwicker, Xinhai Liu, Yu-Shen Liu, Zhizhong Han","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-06T15:07:03Z","title":"Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02565","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":"sha256:57c81ef6a40925ab3a10ef98fc50c5b981e843ff4ebf9ddcac66e8f1dbacb957","target":"record","created_at":"2026-05-18T00:00:39Z","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":"d25c56fdf15dce6907feb9d87a40f386e0720e2132fa604e28d2620dde456e85","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-06T15:07:03Z","title_canon_sha256":"bd876ef34dbcb337ea68e8b4eaa9c7e94c9ead7b0f63f9cef770a6ca643b8656"},"schema_version":"1.0","source":{"id":"1811.02565","kind":"arxiv","version":2}},"canonical_sha256":"9ed16e71f22ef22b276050c3b7b8adfaf95f6bf52e47d5b1fb1020b912b82bc5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ed16e71f22ef22b276050c3b7b8adfaf95f6bf52e47d5b1fb1020b912b82bc5","first_computed_at":"2026-05-18T00:00:39.085629Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:00:39.085629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jcazieXBrNnv0vyVE8TvyXgF0TGR0y57Sz60F6p+xvj/uDg0p2/A2leLPPCbVN5y12cWlVgAKeDEjbgaJgPhAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:00:39.086079Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.02565","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:57c81ef6a40925ab3a10ef98fc50c5b981e843ff4ebf9ddcac66e8f1dbacb957","sha256:7623386e93f55e7c8d2a8f82b88a2c20ba54c1c42d8730de5e9112d4ab980422"],"state_sha256":"9bf5f39001d44905869d6242bd17e886ac6125360317e107015c681b5eea98db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kVFq7v8z+DdWzn4zkIVXGfQscXZewzXK1M+gIlj8eqvh0RPKJM0z1yz346lWyKJm04QOwX7+UeqAM4DEtXryBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T07:51:28.840121Z","bundle_sha256":"77aea42b2412198c37592c25b8c0dea19233086de9351c59f83f63e47a4b8f1a"}}