  We present a novel octree-based multi-level framework for large-scale point
cloud compression, which can organize sparse and unstructured point clouds in a
memory-efficient way. In this framework, we propose a new entropy model that
explores the hierarchical dependency in an octree using the context of
siblings' children, ancestors, and neighbors to encode the occupancy
information of each non-leaf octree node into a bitstream. Moreover, we locally
fit quadratic surfaces with a voxel-based geometry-aware module to provide
geometric priors in entropy encoding. These strong priors empower our entropy
framework to encode the octree into a more compact bitstream. In the decoding
stage, we apply a two-step heuristic strategy to restore point clouds with
better reconstruction quality. The quantitative evaluation shows that our
method outperforms state-of-the-art baselines with a bitrate improvement of
11-16% and 12-14% on the KITTI Odometry and nuScenes datasets, respectively.
