{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TMFOLNNR4MQEHPUQZXY6KPMKVA","short_pith_number":"pith:TMFOLNNR","schema_version":"1.0","canonical_sha256":"9b0ae5b5b1e32043be90cdf1e53d8aa82a531238f0ae1ce653467bc6886d9133","source":{"kind":"arxiv","id":"2410.01027","version":1},"attestation_state":"computed","paper":{"title":"Graph-based Scalable Sampling of 3D Point Cloud Attributes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM","eess.SP"],"primary_cat":"eess.IV","authors_text":"Ajinkya Jayawant, Antonio Ortega, Eduardo Pavez, Keisuke Nonaka, Ryosuke Watanabe, Shashank N. Sridhara","submitted_at":"2024-10-01T19:35:07Z","abstract_excerpt":"3D Point clouds (PCs) are commonly used to represent 3D scenes. They can have millions of points, making subsequent downstream tasks such as compression and streaming computationally expensive. PC sampling (selecting a subset of points) can be used to reduce complexity. Existing PC sampling algorithms focus on preserving geometry features and often do not scale to handle large PCs. In this work, we develop scalable graph-based sampling algorithms for PC color attributes, assuming the full geometry is available. Our sampling algorithms are optimized for a signal reconstruction method that minim"},"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":"2410.01027","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-10-01T19:35:07Z","cross_cats_sorted":["cs.MM","eess.SP"],"title_canon_sha256":"c4b6b6708f50668d9daaee95d109f4fd33b10c9ab0c9cc042e9e81a944ee11cc","abstract_canon_sha256":"84fcbd3203f33c5bdfa9ec742e5d61d31e8cb073d4a3e9b1b113ad0d68c71cd6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:14:42.087726Z","signature_b64":"2bimH6fqlTW8bnb38Ys8DaQ6zHjOI8IBxvNX5dwkO8Z+l6WwxfZDOKyM3VPAHqybIFkUnER0oAg3VZuIbrSTDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b0ae5b5b1e32043be90cdf1e53d8aa82a531238f0ae1ce653467bc6886d9133","last_reissued_at":"2026-07-05T09:14:42.087245Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:14:42.087245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph-based Scalable Sampling of 3D Point Cloud Attributes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MM","eess.SP"],"primary_cat":"eess.IV","authors_text":"Ajinkya Jayawant, Antonio Ortega, Eduardo Pavez, Keisuke Nonaka, Ryosuke Watanabe, Shashank N. Sridhara","submitted_at":"2024-10-01T19:35:07Z","abstract_excerpt":"3D Point clouds (PCs) are commonly used to represent 3D scenes. They can have millions of points, making subsequent downstream tasks such as compression and streaming computationally expensive. PC sampling (selecting a subset of points) can be used to reduce complexity. Existing PC sampling algorithms focus on preserving geometry features and often do not scale to handle large PCs. In this work, we develop scalable graph-based sampling algorithms for PC color attributes, assuming the full geometry is available. Our sampling algorithms are optimized for a signal reconstruction method that minim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.01027","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/2410.01027/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":"2410.01027","created_at":"2026-07-05T09:14:42.087301+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.01027v1","created_at":"2026-07-05T09:14:42.087301+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.01027","created_at":"2026-07-05T09:14:42.087301+00:00"},{"alias_kind":"pith_short_12","alias_value":"TMFOLNNR4MQE","created_at":"2026-07-05T09:14:42.087301+00:00"},{"alias_kind":"pith_short_16","alias_value":"TMFOLNNR4MQEHPUQ","created_at":"2026-07-05T09:14:42.087301+00:00"},{"alias_kind":"pith_short_8","alias_value":"TMFOLNNR","created_at":"2026-07-05T09:14:42.087301+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.09753","citing_title":"Towards joint graph learning and sampling set selection from data","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA","json":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA.json","graph_json":"https://pith.science/api/pith-number/TMFOLNNR4MQEHPUQZXY6KPMKVA/graph.json","events_json":"https://pith.science/api/pith-number/TMFOLNNR4MQEHPUQZXY6KPMKVA/events.json","paper":"https://pith.science/paper/TMFOLNNR"},"agent_actions":{"view_html":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA","download_json":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA.json","view_paper":"https://pith.science/paper/TMFOLNNR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.01027&json=true","fetch_graph":"https://pith.science/api/pith-number/TMFOLNNR4MQEHPUQZXY6KPMKVA/graph.json","fetch_events":"https://pith.science/api/pith-number/TMFOLNNR4MQEHPUQZXY6KPMKVA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA/action/storage_attestation","attest_author":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA/action/author_attestation","sign_citation":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA/action/citation_signature","submit_replication":"https://pith.science/pith/TMFOLNNR4MQEHPUQZXY6KPMKVA/action/replication_record"}},"created_at":"2026-07-05T09:14:42.087301+00:00","updated_at":"2026-07-05T09:14:42.087301+00:00"}