The ever-increasing 3D application makes the point cloud compression unprecedentedly important and needed. In this paper, we propose a patch-based compression process using deep learning, focusing on the lossy point cloud geometry compression. Unlike existing point cloud compression networks, which apply feature extraction and reconstruction on the entire point cloud, we divide the point cloud into patches and compress each patch independently. In the decoding process, we finally assemble the decompressed patches into a complete point cloud. In addition, we train our network by a patch-to-patch criterion, i.e., use the local reconstruction loss for optimization, to approximate the global reconstruction optimality. Our method outperforms the state-of-the-art in terms of rate-distortion performance, especially at low bitrates. Moreover, the compression process we proposed can guarantee to generate the same number of points as the input. The network model of this method can be easily applied to other point cloud reconstruction problems, such as upsampling.

本文提出了一种基于块的压缩算法，使用深度学习对点云的几何信息进行有损压缩，并取得了优于现有算法的压缩比。该算法在编解码时将点云划分为块进行压缩，使用局部重构误差作为优化标准，保证压缩后的点数与原始数据相等。同时，该算法的网络模型还可方便地应用于同类问题，例如点云上采样。

点云几何压缩的基于补丁的深度自编码器