{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MSCXIXNPRYF4OWTWPH2XVPIZGJ","short_pith_number":"pith:MSCXIXNP","schema_version":"1.0","canonical_sha256":"6485745daf8e0bc75a7679f57abd1932730d51e82d6ff9b8c24b2327ccdce1d5","source":{"kind":"arxiv","id":"2409.10293","version":1},"attestation_state":"computed","paper":{"title":"SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hui Yuan, Raouf Hamzaoui, Sam Kwong, Shiqi Jiang, Tian Guo, Xiaolong Mao","submitted_at":"2024-09-16T13:59:43Z","abstract_excerpt":"We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for fe"},"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":"2409.10293","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-09-16T13:59:43Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"d0a191e7c0bf9475fc61131fb918d698368242b303cd694e55959ac33fac200d","abstract_canon_sha256":"070861ccd656120b9a00462d50ad8434d9d4a9b791395cca680b014fa3d2870f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:38.060711Z","signature_b64":"j8Qo7k2YbKMnOjsQpO5HQtQ7vd66rSIA4cIB7JlK1VWxCtrI8JNiuZkwEhgx1oQ6hvqh3zq2eK0VM65jU5iDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6485745daf8e0bc75a7679f57abd1932730d51e82d6ff9b8c24b2327ccdce1d5","last_reissued_at":"2026-07-05T09:07:38.060255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:38.060255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hui Yuan, Raouf Hamzaoui, Sam Kwong, Shiqi Jiang, Tian Guo, Xiaolong Mao","submitted_at":"2024-09-16T13:59:43Z","abstract_excerpt":"We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with geometry assistance, and a global hyperprior entropy model. The frequency sampling module uses a Hamming window and the Fast Fourier Transform to extract high-frequency components of the point cloud. The difference between the original point cloud and the sampled point cloud is divided into multiple sub-point clouds. These sub-point clouds are then partitioned using an octree, providing a structured input for fe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.10293","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/2409.10293/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":"2409.10293","created_at":"2026-07-05T09:07:38.060309+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.10293v1","created_at":"2026-07-05T09:07:38.060309+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.10293","created_at":"2026-07-05T09:07:38.060309+00:00"},{"alias_kind":"pith_short_12","alias_value":"MSCXIXNPRYF4","created_at":"2026-07-05T09:07:38.060309+00:00"},{"alias_kind":"pith_short_16","alias_value":"MSCXIXNPRYF4OWTW","created_at":"2026-07-05T09:07:38.060309+00:00"},{"alias_kind":"pith_short_8","alias_value":"MSCXIXNP","created_at":"2026-07-05T09:07:38.060309+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/MSCXIXNPRYF4OWTWPH2XVPIZGJ","json":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ.json","graph_json":"https://pith.science/api/pith-number/MSCXIXNPRYF4OWTWPH2XVPIZGJ/graph.json","events_json":"https://pith.science/api/pith-number/MSCXIXNPRYF4OWTWPH2XVPIZGJ/events.json","paper":"https://pith.science/paper/MSCXIXNP"},"agent_actions":{"view_html":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ","download_json":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ.json","view_paper":"https://pith.science/paper/MSCXIXNP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.10293&json=true","fetch_graph":"https://pith.science/api/pith-number/MSCXIXNPRYF4OWTWPH2XVPIZGJ/graph.json","fetch_events":"https://pith.science/api/pith-number/MSCXIXNPRYF4OWTWPH2XVPIZGJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ/action/storage_attestation","attest_author":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ/action/author_attestation","sign_citation":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ/action/citation_signature","submit_replication":"https://pith.science/pith/MSCXIXNPRYF4OWTWPH2XVPIZGJ/action/replication_record"}},"created_at":"2026-07-05T09:07:38.060309+00:00","updated_at":"2026-07-05T09:07:38.060309+00:00"}