{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:HWCYQMJ4RE67C7Y7A7QYRIVPQL","short_pith_number":"pith:HWCYQMJ4","schema_version":"1.0","canonical_sha256":"3d8588313c893df17f1f07e188a2af82c1948a486d6943acf176a68cc03e303b","source":{"kind":"arxiv","id":"2211.10916","version":4},"attestation_state":"computed","paper":{"title":"ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Tongda Xu, Yan Wang, Yiqi Jin, Yuhuan Lin, Ziyu Zhu","submitted_at":"2022-11-20T09:20:32Z","abstract_excerpt":"Recently, deep learning methods have shown promising results in point cloud compression. For octree-based point cloud compression, previous works show that the information of ancestor nodes and sibling nodes are equally important for predicting current node. However, those works either adopt insufficient context or bring intolerable decoding complexity (e.g. >600s). To address this problem, we propose a sufficient yet efficient context model and design an efficient deep learning codec for point clouds. Specifically, we first propose a window-constrained multi-group coding strategy to exploit t"},"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":"2211.10916","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-11-20T09:20:32Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"bbc28e112015bc1741f8c6ce5896bc8465873c9947ff78583c8b8cc280c8cfbc","abstract_canon_sha256":"939fd2a7fe9ce8f9b0c87d2edcce00b6e5e049d9e870c10a7d3af72df7548e0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:14.144182Z","signature_b64":"68bTNp9LMgmXN6rsX24/Q5aK/eDfOjc0NP78T/AfgFCpYtRgEZcY+VPXdmf2F0+FZht6i+0uEQRNFuxefHnFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d8588313c893df17f1f07e188a2af82c1948a486d6943acf176a68cc03e303b","last_reissued_at":"2026-07-05T07:22:14.143723Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:14.143723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ECM-OPCC: Efficient Context Model for Octree-based Point Cloud Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Tongda Xu, Yan Wang, Yiqi Jin, Yuhuan Lin, Ziyu Zhu","submitted_at":"2022-11-20T09:20:32Z","abstract_excerpt":"Recently, deep learning methods have shown promising results in point cloud compression. For octree-based point cloud compression, previous works show that the information of ancestor nodes and sibling nodes are equally important for predicting current node. However, those works either adopt insufficient context or bring intolerable decoding complexity (e.g. >600s). To address this problem, we propose a sufficient yet efficient context model and design an efficient deep learning codec for point clouds. Specifically, we first propose a window-constrained multi-group coding strategy to exploit t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.10916","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/2211.10916/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":"2211.10916","created_at":"2026-07-05T07:22:14.143797+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.10916v4","created_at":"2026-07-05T07:22:14.143797+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.10916","created_at":"2026-07-05T07:22:14.143797+00:00"},{"alias_kind":"pith_short_12","alias_value":"HWCYQMJ4RE67","created_at":"2026-07-05T07:22:14.143797+00:00"},{"alias_kind":"pith_short_16","alias_value":"HWCYQMJ4RE67C7Y7","created_at":"2026-07-05T07:22:14.143797+00:00"},{"alias_kind":"pith_short_8","alias_value":"HWCYQMJ4","created_at":"2026-07-05T07:22:14.143797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.04737","citing_title":"LEAN-3D: Low-latency Hierarchical Point Cloud Codec for Mobile 3D Streaming","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL","json":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL.json","graph_json":"https://pith.science/api/pith-number/HWCYQMJ4RE67C7Y7A7QYRIVPQL/graph.json","events_json":"https://pith.science/api/pith-number/HWCYQMJ4RE67C7Y7A7QYRIVPQL/events.json","paper":"https://pith.science/paper/HWCYQMJ4"},"agent_actions":{"view_html":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL","download_json":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL.json","view_paper":"https://pith.science/paper/HWCYQMJ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.10916&json=true","fetch_graph":"https://pith.science/api/pith-number/HWCYQMJ4RE67C7Y7A7QYRIVPQL/graph.json","fetch_events":"https://pith.science/api/pith-number/HWCYQMJ4RE67C7Y7A7QYRIVPQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL/action/storage_attestation","attest_author":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL/action/author_attestation","sign_citation":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL/action/citation_signature","submit_replication":"https://pith.science/pith/HWCYQMJ4RE67C7Y7A7QYRIVPQL/action/replication_record"}},"created_at":"2026-07-05T07:22:14.143797+00:00","updated_at":"2026-07-05T07:22:14.143797+00:00"}