pith:BPLFIZV7
PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
PACE decouples context aggregation from probability prediction to cut latency in LiDAR compression.
arxiv:2605.01320 v2 · 2026-05-02 · cs.CV
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Record completeness
Claims
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.
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.
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.
Receipt and verification
| First computed | 2026-06-09T01:05:18.566479Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
0bd65466bf0582398f59515b101933fa54ec82e5ba7ce3f2a9a99bb2af18c1d8
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/BPLFIZV7AWBDTD2ZKFNRAGJT7J \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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