Diverse Context Model for Large-Scale Dynamic Point Cloud Compression

Abstract

Sufficient context is essential for modeling the geometric distribution of large-scale dynamic point clouds. However, previous methods gather the context without considering the distinctive characteristics of different contexts, which leads to suboptimal performances. In this paper, we propose an octree-based diverse context model that captures the large-scale context, local detailed context, and temporal context adaptively and separately. To effectively aggregate the large-scale context, we exploit large-range sibling and ancestor nodes with a dilated mask window. For the local detailed context, we aggregate adjacent encoded sibling nodes with a subsequent mask window. To incorporate temporal context, we propose a density network to take full advantage of the cross-frame information of dynamic point clouds. Experiments on LiDAR and dense object datasets show that our method saves 38.17% and 47.47% of bitrates compared to the MPEG G-PCC method, respectively.