{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:VGXJ7MUHKYI3LPYTGO5ETEOGVS","short_pith_number":"pith:VGXJ7MUH","schema_version":"1.0","canonical_sha256":"a9ae9fb2875611b5bf1333ba4991c6ac919a10e2fe94b2be1e87f1657b730c1c","source":{"kind":"arxiv","id":"2012.11895","version":4},"attestation_state":"computed","paper":{"title":"Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Le Yang, Qi Yang, Yiling Xu, Yipeng Liu","submitted_at":"2020-12-22T09:29:10Z","abstract_excerpt":"Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years. However, in many cases, obtaining the reference point clouds is difficult, so no-reference (NR) metrics have become a research hotspot. Few researches about NR-PCQA are carried out due to the lack of a large-scale PCQA dataset. In this paper, we first build a large-scale PCQA dataset named LS-PCQA, which includes 104 reference point clouds and more than 22,000 distorted samples. In the dataset, each reference point cloud is augmented with 31 types of impairments (e.g., Gaussian noise, co"},"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":"2012.11895","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-12-22T09:29:10Z","cross_cats_sorted":[],"title_canon_sha256":"07b3a455ba8184996f193c8c4a82ac8a68317f8bc4d60cc2a58c010f17b55d96","abstract_canon_sha256":"e2a93b53fc586d0de1d2d25783fcd40b3d9359f8186667ee080ecabf0a7848b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:42:30.097381Z","signature_b64":"fpsEYd2kiKrKR8H1EkwNs6z6s/vEjpnIceoZ4P7vMsFrWUw+V6aOd6fH7Gl0P4PE74DqxsIC0hfwQcjdyoXYDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9ae9fb2875611b5bf1333ba4991c6ac919a10e2fe94b2be1e87f1657b730c1c","last_reissued_at":"2026-07-05T04:42:30.096959Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:42:30.096959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Le Yang, Qi Yang, Yiling Xu, Yipeng Liu","submitted_at":"2020-12-22T09:29:10Z","abstract_excerpt":"Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years. However, in many cases, obtaining the reference point clouds is difficult, so no-reference (NR) metrics have become a research hotspot. Few researches about NR-PCQA are carried out due to the lack of a large-scale PCQA dataset. In this paper, we first build a large-scale PCQA dataset named LS-PCQA, which includes 104 reference point clouds and more than 22,000 distorted samples. In the dataset, each reference point cloud is augmented with 31 types of impairments (e.g., Gaussian noise, co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.11895","kind":"arxiv","version":4},"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/2012.11895/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":"2012.11895","created_at":"2026-07-05T04:42:30.097021+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.11895v4","created_at":"2026-07-05T04:42:30.097021+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.11895","created_at":"2026-07-05T04:42:30.097021+00:00"},{"alias_kind":"pith_short_12","alias_value":"VGXJ7MUHKYI3","created_at":"2026-07-05T04:42:30.097021+00:00"},{"alias_kind":"pith_short_16","alias_value":"VGXJ7MUHKYI3LPYT","created_at":"2026-07-05T04:42:30.097021+00:00"},{"alias_kind":"pith_short_8","alias_value":"VGXJ7MUH","created_at":"2026-07-05T04:42:30.097021+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.13387","citing_title":"From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training","ref_index":48,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS","json":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS.json","graph_json":"https://pith.science/api/pith-number/VGXJ7MUHKYI3LPYTGO5ETEOGVS/graph.json","events_json":"https://pith.science/api/pith-number/VGXJ7MUHKYI3LPYTGO5ETEOGVS/events.json","paper":"https://pith.science/paper/VGXJ7MUH"},"agent_actions":{"view_html":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS","download_json":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS.json","view_paper":"https://pith.science/paper/VGXJ7MUH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.11895&json=true","fetch_graph":"https://pith.science/api/pith-number/VGXJ7MUHKYI3LPYTGO5ETEOGVS/graph.json","fetch_events":"https://pith.science/api/pith-number/VGXJ7MUHKYI3LPYTGO5ETEOGVS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS/action/storage_attestation","attest_author":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS/action/author_attestation","sign_citation":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS/action/citation_signature","submit_replication":"https://pith.science/pith/VGXJ7MUHKYI3LPYTGO5ETEOGVS/action/replication_record"}},"created_at":"2026-07-05T04:42:30.097021+00:00","updated_at":"2026-07-05T04:42:30.097021+00:00"}