{"paper":{"title":"PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dandan Ding, Jiahao Zhu, Kang You, Zhan Ma","submitted_at":"2026-05-02T08:36:11Z","abstract_excerpt":"LiDAR point cloud compression is vital for autonomous systems to handle massive data from high-resolution sensors. While learned entropy modeling built upon octree structures yields high compression gains, it faces two critical bottlenecks: 1) prohibitive latency, particularly during decoding, caused by causal, multi-stage context modeling; and 2) a rigid performance-latency trade-off, preventing a single model from adapting to varying constraints. These limitations stem from the tight coupling between the context aggregation backbone and probability prediction. To address this, we propose PAC"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"PACE sets a new state-of-the-art in compression efficiency, achieving notable BD-BR savings and reducing decoding latency by over 90% in autoregressive mode.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The non-causal backbone can still provide sufficient context for accurate probability prediction when causality is confined to the lightweight predictor, without loss of modeling power from the original tight coupling.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4ef7ebd58a9e0ada4fa006978eb478084e55479d86473d26d064482a80a31bbf"},"source":{"id":"2605.01320","kind":"arxiv","version":2},"verdict":{"id":"979c80f0-1f05-4c1d-be0b-9a2af5d019ee","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T14:31:33.574342Z","strongest_claim":"PACE sets a new state-of-the-art in compression efficiency, achieving notable BD-BR savings and reducing decoding latency by over 90% in autoregressive mode.","one_line_summary":"PACE achieves state-of-the-art LiDAR point cloud compression with over 90% lower decoding latency by using a non-causal backbone and a stage-scalable causal predictor.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The non-causal backbone can still provide sufficient context for accurate probability prediction when causality is confined to the lightweight predictor, without loss of modeling power from the original tight coupling.","pith_extraction_headline":"PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.01320/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T18:35:15.090729Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T17:21:21.493926Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ff8f8f50c77f4f0f3adeec4e21ebe220699232540e66361156cfb6a732bc0e48"},"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"}