Efficient point cloud compression is fundamental to enable the deployment of
virtual and mixed reality applications, since the number of points to code can
range in the order of millions. In this paper, we present a novel data-driven
geometry compression method for static point clouds based on learned
convolutional transforms and uniform quantization. We perform joint
optimization of both rate and distortion using a trade-off parameter. In
addition, we cast the decoding process as a binary classification of the point
cloud occupancy map. Our method outperforms the MPEG reference solution in
terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset with
51.5% BDBR savings on average. Moreover, while octree-based methods face
exponential diminution of the number of points at low bitrates, our method
still produces high resolution outputs even at low bitrates. Code and
supplementary material are available at
this https URL .

本篇论文介绍了基于学习的卷积变换和均匀量化的静态点云数据几何压缩方法，并将解码过程视为点云占用图的二元分类。该方法在 Microsoft Voxelized Upper Bodies 数据集上表现优异，平均 BDBR 节省率达 51.5%，且能够在低比特率下仍产生高分辨率的输出。