AI RESEARCH
PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
arXiv CS.CV
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ArXi:2605.01320v1 Announce Type: new 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.