{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EQKZ6UR5FHFH5PVUCMXW6RRMBV","short_pith_number":"pith:EQKZ6UR5","schema_version":"1.0","canonical_sha256":"24159f523d29ca7ebeb4132f6f462c0d6b4db30516503e01487c2e6b5ffa8dea","source":{"kind":"arxiv","id":"2507.15686","version":1},"attestation_state":"computed","paper":{"title":"LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"He Huang, Qi Yang, Shuting Xia, Wenjie Huang, Yiling Xu, Zhu Li","submitted_at":"2025-07-21T14:48:54Z","abstract_excerpt":"Existing AI-based point cloud compression methods struggle with dependence on specific training data distributions, which limits their real-world deployment. Implicit Neural Representation (INR) methods solve the above problem by encoding overfitted network parameters to the bitstream, resulting in more distribution-agnostic results. However, due to the limitation of encoding time and decoder size, current INR based methods only consider lossy geometry compression. In this paper, we propose the first INR based lossless point cloud geometry compression method called Lossless Implicit Neural Rep"},"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":"2507.15686","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-21T14:48:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"477f296d13033bf282b0e133dfb585de80d64a1a6fee7563ee3f86a3a88ec5f5","abstract_canon_sha256":"87461c49422bca4856f41dfc21fdb8bc1aa2ede16a183f02c793cf17c1e28f48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:42.077559Z","signature_b64":"YqTk8CqfsgmpzVR1o+vLIfujbdujfgCYERmvZazMvN+xPU0laoXbkSJ0jQx40T3MWY5Z/UchfnYhvnZjqEW2DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24159f523d29ca7ebeb4132f6f462c0d6b4db30516503e01487c2e6b5ffa8dea","last_reissued_at":"2026-07-05T11:40:42.077066Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:42.077066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"He Huang, Qi Yang, Shuting Xia, Wenjie Huang, Yiling Xu, Zhu Li","submitted_at":"2025-07-21T14:48:54Z","abstract_excerpt":"Existing AI-based point cloud compression methods struggle with dependence on specific training data distributions, which limits their real-world deployment. Implicit Neural Representation (INR) methods solve the above problem by encoding overfitted network parameters to the bitstream, resulting in more distribution-agnostic results. However, due to the limitation of encoding time and decoder size, current INR based methods only consider lossy geometry compression. In this paper, we propose the first INR based lossless point cloud geometry compression method called Lossless Implicit Neural Rep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.15686","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/2507.15686/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":"2507.15686","created_at":"2026-07-05T11:40:42.077118+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.15686v1","created_at":"2026-07-05T11:40:42.077118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.15686","created_at":"2026-07-05T11:40:42.077118+00:00"},{"alias_kind":"pith_short_12","alias_value":"EQKZ6UR5FHFH","created_at":"2026-07-05T11:40:42.077118+00:00"},{"alias_kind":"pith_short_16","alias_value":"EQKZ6UR5FHFH5PVU","created_at":"2026-07-05T11:40:42.077118+00:00"},{"alias_kind":"pith_short_8","alias_value":"EQKZ6UR5","created_at":"2026-07-05T11:40:42.077118+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/EQKZ6UR5FHFH5PVUCMXW6RRMBV","json":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV.json","graph_json":"https://pith.science/api/pith-number/EQKZ6UR5FHFH5PVUCMXW6RRMBV/graph.json","events_json":"https://pith.science/api/pith-number/EQKZ6UR5FHFH5PVUCMXW6RRMBV/events.json","paper":"https://pith.science/paper/EQKZ6UR5"},"agent_actions":{"view_html":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV","download_json":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV.json","view_paper":"https://pith.science/paper/EQKZ6UR5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.15686&json=true","fetch_graph":"https://pith.science/api/pith-number/EQKZ6UR5FHFH5PVUCMXW6RRMBV/graph.json","fetch_events":"https://pith.science/api/pith-number/EQKZ6UR5FHFH5PVUCMXW6RRMBV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV/action/storage_attestation","attest_author":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV/action/author_attestation","sign_citation":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV/action/citation_signature","submit_replication":"https://pith.science/pith/EQKZ6UR5FHFH5PVUCMXW6RRMBV/action/replication_record"}},"created_at":"2026-07-05T11:40:42.077118+00:00","updated_at":"2026-07-05T11:40:42.077118+00:00"}