{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:A5PWBJJ4ASMXTH6WHUVLBCMBMI","short_pith_number":"pith:A5PWBJJ4","schema_version":"1.0","canonical_sha256":"075f60a53c0499799fd63d2ab08981623fe7bb67e8c1d4accba805c090a1ffa5","source":{"kind":"arxiv","id":"2211.01110","version":2},"attestation_state":"computed","paper":{"title":"AS-PD: An Arbitrary-Size Downsampling Framework for Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Jinsheng Sun, Peng Zhang, Ruoyin Xie, Weiqing Li, Zhiyong Su","submitted_at":"2022-11-02T13:37:16Z","abstract_excerpt":"Point cloud downsampling is a crucial pre-processing operation to downsample points in order to unify data size and reduce computational cost, to name a few. Recent research on point cloud downsampling has achieved great success which concentrates on learning to sample in a task-aware way. However, existing learnable samplers can not directly perform arbitrary-size downsampling, and assume the input size is fixed. In this paper, we introduce the AS-PD, a novel task-aware sampling framework that directly downsamples point clouds to any smaller size based on a sample-to-refine strategy. Given an"},"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":"2211.01110","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-02T13:37:16Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"7dd50fce48b7fb49af642417c2c8894767983228c98529f51f571a5822c4f53a","abstract_canon_sha256":"af69327a3c29b614ef019e78f2017ca48df0b9a5a34a236dc5834cf0f717bf2c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:31:57.833866Z","signature_b64":"MLXYOfZhqOdjCh5ikRuiVVl8yOF7u9s7uZwgB2clqkC+t/NBPPqRXlUO+AZoErQXWmt2I/Kyg3OO0MyZlFypCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"075f60a53c0499799fd63d2ab08981623fe7bb67e8c1d4accba805c090a1ffa5","last_reissued_at":"2026-07-05T05:31:57.833420Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:31:57.833420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AS-PD: An Arbitrary-Size Downsampling Framework for Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Jinsheng Sun, Peng Zhang, Ruoyin Xie, Weiqing Li, Zhiyong Su","submitted_at":"2022-11-02T13:37:16Z","abstract_excerpt":"Point cloud downsampling is a crucial pre-processing operation to downsample points in order to unify data size and reduce computational cost, to name a few. Recent research on point cloud downsampling has achieved great success which concentrates on learning to sample in a task-aware way. However, existing learnable samplers can not directly perform arbitrary-size downsampling, and assume the input size is fixed. In this paper, we introduce the AS-PD, a novel task-aware sampling framework that directly downsamples point clouds to any smaller size based on a sample-to-refine strategy. Given an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.01110","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/2211.01110/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":"2211.01110","created_at":"2026-07-05T05:31:57.833480+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.01110v2","created_at":"2026-07-05T05:31:57.833480+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.01110","created_at":"2026-07-05T05:31:57.833480+00:00"},{"alias_kind":"pith_short_12","alias_value":"A5PWBJJ4ASMX","created_at":"2026-07-05T05:31:57.833480+00:00"},{"alias_kind":"pith_short_16","alias_value":"A5PWBJJ4ASMXTH6W","created_at":"2026-07-05T05:31:57.833480+00:00"},{"alias_kind":"pith_short_8","alias_value":"A5PWBJJ4","created_at":"2026-07-05T05:31:57.833480+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/A5PWBJJ4ASMXTH6WHUVLBCMBMI","json":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI.json","graph_json":"https://pith.science/api/pith-number/A5PWBJJ4ASMXTH6WHUVLBCMBMI/graph.json","events_json":"https://pith.science/api/pith-number/A5PWBJJ4ASMXTH6WHUVLBCMBMI/events.json","paper":"https://pith.science/paper/A5PWBJJ4"},"agent_actions":{"view_html":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI","download_json":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI.json","view_paper":"https://pith.science/paper/A5PWBJJ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.01110&json=true","fetch_graph":"https://pith.science/api/pith-number/A5PWBJJ4ASMXTH6WHUVLBCMBMI/graph.json","fetch_events":"https://pith.science/api/pith-number/A5PWBJJ4ASMXTH6WHUVLBCMBMI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI/action/storage_attestation","attest_author":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI/action/author_attestation","sign_citation":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI/action/citation_signature","submit_replication":"https://pith.science/pith/A5PWBJJ4ASMXTH6WHUVLBCMBMI/action/replication_record"}},"created_at":"2026-07-05T05:31:57.833480+00:00","updated_at":"2026-07-05T05:31:57.833480+00:00"}