Point clouds have become increasingly vital across various applications
thanks to their ability to realistically depict 3D objects and scenes.
Nevertheless, effectively compressing unstructured, high-precision point cloud
data remains a significant challenge. In this paper, we present a pioneering
point cloud compression framework capable of handling both geometry and
attribute components. Unlike traditional approaches and existing learning-based
methods, our framework utilizes two coordinate-based neural networks to
implicitly represent a voxelized point cloud. The first network generates the
occupancy status of a voxel, while the second network determines the attributes
of an occupied voxel. To tackle an immense number of voxels within the
volumetric space, we partition the space into smaller cubes and focus solely on
voxels within non-empty cubes. By feeding the coordinates of these voxels into
the respective networks, we reconstruct the geometry and attribute components
of the original point cloud. The neural network parameters are further
quantized and compressed. Experimental results underscore the superior
performance of our proposed method compared to the octree-based approach
employed in the latest G-PCC standards. Moreover, our method exhibits high
universality when contrasted with existing learning-based techniques.

本论文介绍了一个能够处理几何和属性组件的点云压缩框架，利用两个基于坐标的神经网络来隐式表示体素化的点云，通过将空间划分为小的立方体并专注于非空立方体内的体素来重建原始点云的几何和属性组件。实验结果表明，与最新的 G-PCC 标准中采用的八叉树方法相比，我们提出的方法具有更优越的性能，并且与现有的基于学习的技术相比具有高度的普适性。