Abstract
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.
Abstract (translated)
高效的点云压缩是实现虚拟和混合现实应用程序部署的基础，因为点到代码的数量可以达到数百万。本文提出了一种基于学习卷积变换和均匀量化的静态点云数据驱动几何压缩方法。我们使用权衡参数对速率和失真进行联合优化。此外，我们将解码过程转换为点云占用地图的二进制分类。我们的方法在微软体素化上体数据集的速率失真方面优于MPEG参考解决方案，平均节省51.5%的BDBR。此外，虽然基于八叉树的方法在低比特率时面临着点数的指数减少，但即使在低比特率时，我们的方法仍能产生高分辨率输出。
URL
https://arxiv.org/abs/1903.08548
