提出PACE框架，显著降低激光雷达点云压缩解码延迟。
PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
- 将上下文聚合改为非因果结构，仅在预测器中保留因果性。
- 解码延迟降低90%以上，压缩效率达新SOTA。
- 支持任意预测阶段数，灵活适配不同性能需求。
激光雷达点云压缩对自动驾驶系统处理高分辨率传感器的海量数据至关重要。基于八叉树结构的深度学习熵建模虽能实现高效压缩，但存在两大瓶颈：一是解码时因因果性多阶段上下文建模导致的极高延迟；二是性能与延迟间僵化的权衡，使单一模型难以适应不同约束。根源在于上下文聚合主干与概率预测的紧密耦合。为此，我们提出PACE框架，将祖先上下文聚合重构为非因果主干，仅将因果性保留在轻量、可扩展的预测器中，消除重复执行主干的开销，降低计算负担。预测器支持任意数量的预测阶段，可在不重新加载参数的情况下无缝适应多种性能-延迟权衡。实验表明，PACE在压缩效率上达到新SOTA，自回归模式下解码延迟降低超90%，极具实用价值。
原文摘要 · Abstract (English)
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 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, enabling 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, making it attractive for practical applications.
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