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

Patch-Based Deep Autoencoder for Point Cloud Geometry Compression

Recent deep learning models outperform standard lossy image compression
codecs. However, applying these models on a patch-by-patch basis requires that
each image patch be encoded and decoded independently. The influence from
adjacent patches is therefore lost, leading to block artefacts at low bitrates.
We propose the Binary Inpainting Network (BINet), an autoencoder framework
which incorporates binary inpainting to reinstate interdependencies between
adjacent patches, for improved patch-based compression of still images. When
decoding a patch, BINet additionally uses the binarised encodings from
surrounding patches to guide its reconstruction. In contrast to sequential
inpainting methods where patches are decoded based on previons reconstructions,
BINet operates directly on the binary codes of surrounding patches without
access to the original or reconstructed image data. Encoding and decoding can
therefore be performed in parallel. We demonstrate that BINet improves the
compression quality of a competitive deep image codec across a range of
compression levels.

研究使用深度学习模型用于图像压缩且提出基于瓶颈自填充神经网络框架（BINet）的自编码器，该框架可以通过补全相邻补丁之间的依赖关系以提高补丁式压缩的压缩质量.