  We present a novel compression algorithm for reducing the storage of LiDAR
sensor data streams. Our model exploits spatio-temporal relationships across
multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity
values. Towards this goal, we propose a novel conditional entropy model that
models the probabilities of the octree symbols by considering both coarse level
geometry and previous sweeps' geometric and intensity information. We then use
the learned probability to encode the full data stream into a compact one. Our
experiments demonstrate that our method significantly reduces the joint
geometry and intensity bitrate over prior state-of-the-art LiDAR compression
methods, with a reduction of 7-17% and 15-35% on the UrbanCity and
SemanticKITTI datasets respectively.
