  The non-uniform distribution and extremely sparse nature of the LiDAR point
cloud (LPC) bring significant challenges to its high-efficient compression.
This paper proposes a novel end-to-end, fully-factorized deep framework that
encodes the original LPC into an octree structure and hierarchically decomposes
the octree entropy model in layers. The proposed framework utilizes a
hierarchical latent variable as side information to encapsulate the sibling and
ancestor dependence, which provides sufficient context information for the
modelling of point cloud distribution while enabling the parallel encoding and
decoding of octree nodes in the same layer. Besides, we propose a residual
coding framework for the compression of the latent variable, which explores the
spatial correlation of each layer by progressive downsampling, and model the
corresponding residual with a fully-factorized entropy model. Furthermore, we
propose soft addition and subtraction for residual coding to improve network
flexibility. The comprehensive experiment results on the LiDAR benchmark
SemanticKITTI and MPEG-specified dataset Ford demonstrates that our proposed
framework achieves state-of-the-art performance among all the previous LPC
frameworks. Besides, our end-to-end, fully-factorized framework is proved by
experiment to be high-parallelized and time-efficient and saves more than 99.8%
of decoding time compared to previous state-of-the-art methods on LPC
compression.
