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.

翻译：点云因其能够真实描绘三维物体与场景的能力，在各类应用中日益重要。然而，有效压缩非结构化、高精度点云数据仍是一项重大挑战。本文提出一种开创性的点云压缩框架，可同时处理几何与属性分量。与现有传统方法及基于学习的技术不同，本框架采用两个基于坐标的神经网络来隐式表示体素化点云：首个网络生成体素的占据状态，第二个网络确定占据体素的属性。为应对三维空间中海量体素，我们将空间划分为更小的立方体，仅关注非空立方体内的体素。通过将这些体素的坐标输入对应网络，即可重构原始点云的几何与属性分量。神经网络参数进一步经量化与压缩处理。实验结果表明，相较于最新G-PCC标准采用的八叉树方法，本方法展现出卓越性能。此外，与现有基于学习的技术相比，本方法具有高度通用性。