{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OQQDO7JPLYPDT3TOWX2JENHODD","short_pith_number":"pith:OQQDO7JP","schema_version":"1.0","canonical_sha256":"7420377d2f5e1e39ee6eb5f49234ee18d898407be909adf27ca98350be816793","source":{"kind":"arxiv","id":"2401.08210","version":1},"attestation_state":"computed","paper":{"title":"ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mengyuan Liu, Shen Zhao, Xia Li, Xiangtai Li, Zhongbin Fang","submitted_at":"2024-01-16T08:54:21Z","abstract_excerpt":"Recently, 3D point cloud classification has made significant progress with the help of many datasets. However, these datasets do not reflect the incomplete nature of real-world point clouds caused by occlusion, which limits the practical application of current methods. To bridge this gap, we propose ModelNet-O, a large-scale synthetic dataset of 123,041 samples that emulate real-world point clouds with self-occlusion caused by scanning from monocular cameras. ModelNet-O is 10 times larger than existing datasets and offers more challenging cases to evaluate the robustness of existing methods. O"},"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":"2401.08210","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-16T08:54:21Z","cross_cats_sorted":[],"title_canon_sha256":"0765329f00c6e5c4a9a8a4375fd2cf2d34a98d234c3892e359081fad609df744","abstract_canon_sha256":"7f90cb705ac31554cbca164d52ee80a701a740e946c21200e99777f9860b2cc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:34:09.041465Z","signature_b64":"AZo4mFe3u7kj5UrWRQrop/bO/2gMGc31VCde52I7GsLoEM2QyzcMyuE1xOSs9UfeBuLeQdf97H/PUc9iwyesCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7420377d2f5e1e39ee6eb5f49234ee18d898407be909adf27ca98350be816793","last_reissued_at":"2026-07-05T07:34:09.041076Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:34:09.041076Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mengyuan Liu, Shen Zhao, Xia Li, Xiangtai Li, Zhongbin Fang","submitted_at":"2024-01-16T08:54:21Z","abstract_excerpt":"Recently, 3D point cloud classification has made significant progress with the help of many datasets. However, these datasets do not reflect the incomplete nature of real-world point clouds caused by occlusion, which limits the practical application of current methods. To bridge this gap, we propose ModelNet-O, a large-scale synthetic dataset of 123,041 samples that emulate real-world point clouds with self-occlusion caused by scanning from monocular cameras. ModelNet-O is 10 times larger than existing datasets and offers more challenging cases to evaluate the robustness of existing methods. O"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08210","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.08210/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":"2401.08210","created_at":"2026-07-05T07:34:09.041142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.08210v1","created_at":"2026-07-05T07:34:09.041142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08210","created_at":"2026-07-05T07:34:09.041142+00:00"},{"alias_kind":"pith_short_12","alias_value":"OQQDO7JPLYPD","created_at":"2026-07-05T07:34:09.041142+00:00"},{"alias_kind":"pith_short_16","alias_value":"OQQDO7JPLYPDT3TO","created_at":"2026-07-05T07:34:09.041142+00:00"},{"alias_kind":"pith_short_8","alias_value":"OQQDO7JP","created_at":"2026-07-05T07:34:09.041142+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/OQQDO7JPLYPDT3TOWX2JENHODD","json":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD.json","graph_json":"https://pith.science/api/pith-number/OQQDO7JPLYPDT3TOWX2JENHODD/graph.json","events_json":"https://pith.science/api/pith-number/OQQDO7JPLYPDT3TOWX2JENHODD/events.json","paper":"https://pith.science/paper/OQQDO7JP"},"agent_actions":{"view_html":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD","download_json":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD.json","view_paper":"https://pith.science/paper/OQQDO7JP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.08210&json=true","fetch_graph":"https://pith.science/api/pith-number/OQQDO7JPLYPDT3TOWX2JENHODD/graph.json","fetch_events":"https://pith.science/api/pith-number/OQQDO7JPLYPDT3TOWX2JENHODD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD/action/storage_attestation","attest_author":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD/action/author_attestation","sign_citation":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD/action/citation_signature","submit_replication":"https://pith.science/pith/OQQDO7JPLYPDT3TOWX2JENHODD/action/replication_record"}},"created_at":"2026-07-05T07:34:09.041142+00:00","updated_at":"2026-07-05T07:34:09.041142+00:00"}