{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TT2OVCIQZASCRTXM4BJPN4ALO6","short_pith_number":"pith:TT2OVCIQ","schema_version":"1.0","canonical_sha256":"9cf4ea8910c82428ceece052f6f00b77801a02b340ddd64e37db6067d45ac04b","source":{"kind":"arxiv","id":"2111.10633","version":2},"attestation_state":"computed","paper":{"title":"Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chuntong Cao, Dandan Ding, Jianqiang Wang, Xiaoxing Feng, Zhan Ma, Zhu Li","submitted_at":"2021-11-20T17:02:45Z","abstract_excerpt":"This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor-based voxelized PCG. We call this compression method SparsePCGC. The proposed SparsePCGC is a low complexity solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). The multiscale representation also allows us to compress scale-wise MP-POVs by exploiting cross-scale and same-scale correlations extensively and flexibly. The overall compression efficiency highly depends on the accuracy of estimated occ"},"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":"2111.10633","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-11-20T17:02:45Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"1372430d59e7ee38f0d830800e60714e4b1a2b0480348a6fca498a0b3858e7b9","abstract_canon_sha256":"8eadcd124c9979e294175860f7f8d3fa2f1872e98a238f36e8451dd4f972b443"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:04.244498Z","signature_b64":"ky4SKaXV6j32Vd9UlR5lUulUg4Gg+rAixCuGjkMjUjq19bxw0fDWKYkQaFhvn0oL08NIHH8rWjPpx2hhnC3MDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9cf4ea8910c82428ceece052f6f00b77801a02b340ddd64e37db6067d45ac04b","last_reissued_at":"2026-07-05T05:09:04.243924Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:04.243924Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Chuntong Cao, Dandan Ding, Jianqiang Wang, Xiaoxing Feng, Zhan Ma, Zhu Li","submitted_at":"2021-11-20T17:02:45Z","abstract_excerpt":"This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor-based voxelized PCG. We call this compression method SparsePCGC. The proposed SparsePCGC is a low complexity solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). The multiscale representation also allows us to compress scale-wise MP-POVs by exploiting cross-scale and same-scale correlations extensively and flexibly. The overall compression efficiency highly depends on the accuracy of estimated occ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.10633","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/2111.10633/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":"2111.10633","created_at":"2026-07-05T05:09:04.243989+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.10633v2","created_at":"2026-07-05T05:09:04.243989+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.10633","created_at":"2026-07-05T05:09:04.243989+00:00"},{"alias_kind":"pith_short_12","alias_value":"TT2OVCIQZASC","created_at":"2026-07-05T05:09:04.243989+00:00"},{"alias_kind":"pith_short_16","alias_value":"TT2OVCIQZASCRTXM","created_at":"2026-07-05T05:09:04.243989+00:00"},{"alias_kind":"pith_short_8","alias_value":"TT2OVCIQ","created_at":"2026-07-05T05:09:04.243989+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":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6","json":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6.json","graph_json":"https://pith.science/api/pith-number/TT2OVCIQZASCRTXM4BJPN4ALO6/graph.json","events_json":"https://pith.science/api/pith-number/TT2OVCIQZASCRTXM4BJPN4ALO6/events.json","paper":"https://pith.science/paper/TT2OVCIQ"},"agent_actions":{"view_html":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6","download_json":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6.json","view_paper":"https://pith.science/paper/TT2OVCIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.10633&json=true","fetch_graph":"https://pith.science/api/pith-number/TT2OVCIQZASCRTXM4BJPN4ALO6/graph.json","fetch_events":"https://pith.science/api/pith-number/TT2OVCIQZASCRTXM4BJPN4ALO6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6/action/storage_attestation","attest_author":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6/action/author_attestation","sign_citation":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6/action/citation_signature","submit_replication":"https://pith.science/pith/TT2OVCIQZASCRTXM4BJPN4ALO6/action/replication_record"}},"created_at":"2026-07-05T05:09:04.243989+00:00","updated_at":"2026-07-05T05:09:04.243989+00:00"}