{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OLQ5D7EFURTHO6VAR2RNRHP4DM","short_pith_number":"pith:OLQ5D7EF","schema_version":"1.0","canonical_sha256":"72e1d1fc85a466777aa08ea2d89dfc1b1eced5fe8349d547bfd2f7348c252aba","source":{"kind":"arxiv","id":"2402.11680","version":1},"attestation_state":"computed","paper":{"title":"3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"cs.CV","authors_text":"Bastian Lampe, Lennart Reiher, Lutz Eckstein, Raphael van Kempen, Till Beemelmanns, Timo Woopen, Yuchen Tao","submitted_at":"2024-02-18T19:08:19Z","abstract_excerpt":"Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume. Transforming the raw point cloud data into a dense 2D matrix structure is a promising way for applying compression algorithms. We propose a new lossless and calibrated 3D-to-2D transformation which allows compression algorithms to efficiently exploit spatial correlations within the 2D representation. To compre"},"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":"2402.11680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-02-18T19:08:19Z","cross_cats_sorted":["cs.AI","eess.IV"],"title_canon_sha256":"4fec7496c3ae3097ae372508212f263880d6286471cf83bbd7f317ea8870af7a","abstract_canon_sha256":"b3bec7136caca70557139d7412513d539e7b8ab928e521dd16467146e75e894c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:42.987268Z","signature_b64":"UKgbU8N4GLTMWRGEgRUWbsyirNpuWcvx1Y2R1EvCdulF00W07bIinZBe4puQbj5juf5GTElbDpcdZnplKX/RDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72e1d1fc85a466777aa08ea2d89dfc1b1eced5fe8349d547bfd2f7348c252aba","last_reissued_at":"2026-07-05T07:46:42.986726Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:42.986726Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"cs.CV","authors_text":"Bastian Lampe, Lennart Reiher, Lutz Eckstein, Raphael van Kempen, Till Beemelmanns, Timo Woopen, Yuchen Tao","submitted_at":"2024-02-18T19:08:19Z","abstract_excerpt":"Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume. Transforming the raw point cloud data into a dense 2D matrix structure is a promising way for applying compression algorithms. We propose a new lossless and calibrated 3D-to-2D transformation which allows compression algorithms to efficiently exploit spatial correlations within the 2D representation. To compre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11680","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/2402.11680/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":"2402.11680","created_at":"2026-07-05T07:46:42.986786+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.11680v1","created_at":"2026-07-05T07:46:42.986786+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11680","created_at":"2026-07-05T07:46:42.986786+00:00"},{"alias_kind":"pith_short_12","alias_value":"OLQ5D7EFURTH","created_at":"2026-07-05T07:46:42.986786+00:00"},{"alias_kind":"pith_short_16","alias_value":"OLQ5D7EFURTHO6VA","created_at":"2026-07-05T07:46:42.986786+00:00"},{"alias_kind":"pith_short_8","alias_value":"OLQ5D7EF","created_at":"2026-07-05T07:46:42.986786+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/OLQ5D7EFURTHO6VAR2RNRHP4DM","json":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM.json","graph_json":"https://pith.science/api/pith-number/OLQ5D7EFURTHO6VAR2RNRHP4DM/graph.json","events_json":"https://pith.science/api/pith-number/OLQ5D7EFURTHO6VAR2RNRHP4DM/events.json","paper":"https://pith.science/paper/OLQ5D7EF"},"agent_actions":{"view_html":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM","download_json":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM.json","view_paper":"https://pith.science/paper/OLQ5D7EF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.11680&json=true","fetch_graph":"https://pith.science/api/pith-number/OLQ5D7EFURTHO6VAR2RNRHP4DM/graph.json","fetch_events":"https://pith.science/api/pith-number/OLQ5D7EFURTHO6VAR2RNRHP4DM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM/action/storage_attestation","attest_author":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM/action/author_attestation","sign_citation":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM/action/citation_signature","submit_replication":"https://pith.science/pith/OLQ5D7EFURTHO6VAR2RNRHP4DM/action/replication_record"}},"created_at":"2026-07-05T07:46:42.986786+00:00","updated_at":"2026-07-05T07:46:42.986786+00:00"}