PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
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 c...
| Authors | Jiahao Zhu, Kang You, Dandan Ding et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 May 2026 |
| Last updated at source | 6 June 2026 |
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