PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression

• URL: http://arxiv.org/abs/2605.01320v1
• Date: Sat, 02 May 2026 08:36:11 GMT
• Title: PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
• Abstract summary: LiDAR point cloud compression is vital for autonomous systems to handle massive data from high-resolution sensors.<n>We propose PACE, a new framework that reformulates ancestral context aggregation as a non-causal backbone and causality to a lightweight, stage-scalable predictor.<n>Experiments demonstrate that 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.
• Score: 43.524231862354405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 context aggregation backbone and probability prediction. To address this, we propose PACE, a new framework that reformulates ancestral context aggregation as a non-causal backbone and confines causality to a lightweight, stage-scalable predictor, eliminating repetitive backbone executions and reducing computational overhead. The predictor supports an arbitrary number of prediction stages, supporting seamless adaptation across diverse performance-latency trade-offs without reloading parameters. Experiments demonstrate that 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, highly attractive for practical applications.

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