  This paper presents a novel end-to-end Learned Point Cloud Geometry
Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point
cloud geometry (PCG) using deep neural networks (DNN) based variational
autoencoders (VAE). In our approach, PCG is first voxelized, scaled and
partitioned into non-overlapped 3D cubes, which is then fed into stacked 3D
convolutions for compact latent feature and hyperprior generation. Hyperpriors
are used to improve the conditional probability modeling of latent features. A
weighted binary cross-entropy (WBCE) loss is applied in training while an
adaptive thresholding is used in inference to remove unnecessary voxels and
reduce the distortion. Objectively, our method exceeds the geometry-based point
cloud compression (G-PCC) algorithm standardized by well-known Moving Picture
Experts Group (MPEG) with a significant performance margin, e.g., at least 60%
BD-Rate (Bjontegaard Delta Rate) gains, using common test datasets.
Subjectively, our method has presented better visual quality with smoother
surface reconstruction and appealing details, in comparison to all existing
MPEG standard compliant PCC methods. Our method requires about 2.5MB parameters
in total, which is a fairly small size for practical implementation, even on
embedded platform. Additional ablation studies analyze a variety of aspects
(e.g., cube size, kernels, etc) to explore the application potentials of our
learned-PCGC.
