Point cloud compression (PCC) is a key enabler for various 3-D applications, owing to the universality of the point cloud format. Ideally, 3D point clouds endeavor to depict object/scene surfaces that are continuous. Practically, as a set of discrete samples, point clouds are locally disconnected and sparsely distributed. This sparse nature is hindering the discovery of local correlation among points for compression. Motivated by an analysis with fractal dimension, we propose a heterogeneous approach with deep learning for lossy point cloud geometry compression. On top of a base layer compressing a coarse representation of the input, an enhancement layer is designed to cope with the challenging geometric residual/details. Specifically, a point-based network is applied to convert the erratic local details to latent features residing on the coarse point cloud. Then a sparse convolutional neural network operating on the coarse point cloud is launched. It utilizes the continuity/smoothness of the coarse geometry to compress the latent features as an enhancement bit-stream that greatly benefits the reconstruction quality. When this bit-stream is unavailable, e.g., due to packet loss, we support a skip mode with the same architecture which generates geometric details from the coarse point cloud directly. Experimentation on both dense and sparse point clouds demonstrate the state-of-the-art compression performance achieved by our proposal. Our code is available at https://github.com/InterDigitalInc/GRASP-Net.
翻译:由于点云格式的普遍性, 点云压缩( PCC) 是各种 3D 应用程序的关键推进器 。 理想的情况是, 3D 点云努力描述连续不断的物体/ 表面。 实际上, 作为一组离散的样本, 点云是局部不相连接的, 分散分布的。 这种稀疏的性质阻碍了在压缩点之间发现本地相关性。 受一个分形尺寸分析的驱动, 我们提出一种差异化方法, 深入学习丢失点云的几何测量压缩。 在一个基层压缩输入的粗略表示值时, 一个增强层旨在应对具有挑战性的地理残留/ 尾线表面。 具体地说, 一个基于点的网络网络被用来将不稳定的本地细节转换为位于粗点云层云层上的潜伏特征。 然后启动一个在低点上运行的稀疏松的卷心神经网络。 它利用粗度的几何几何几何几何的几何几何测度来精确度, 大大有利于重建质量 。 当这个位流无法使用时, eg- dread- droadal commarial commagial dreal dreal deal deal deal destration commastration 和我们通过一个可以直接显示的磁度模型来演示。