The universality of the point cloud format enables many 3D applications, making the compression of point clouds a critical phase in practice. Sampled as discrete 3D points, a point cloud approximates 2D surface(s) embedded in 3D with a finite bit-depth. However, the point distribution of a practical point cloud changes drastically as its bit-depth increases, requiring different methodologies for effective consumption/analysis. In this regard, a heterogeneous point cloud compression (PCC) framework is proposed. We unify typical point cloud representations -- point-based, voxel-based, and tree-based representations -- and their associated backbones under a learning-based framework to compress an input point cloud at different bit-depth levels. Having recognized the importance of voxel-domain processing, we augment the framework with a proposed context-aware upsampling for decoding and an enhanced voxel transformer for feature aggregation. Extensive experimentation demonstrates the state-of-the-art performance of our proposal on a wide range of point clouds.

翻译：点云格式的普适性支撑了众多三维应用，使得点云压缩成为实际应用中的关键环节。点云作为离散化三维点的采样，以有限比特深度逼近嵌入三维空间的二维曲面。然而，实际点云的点分布会随着其比特深度的增加而发生剧烈变化，需要采用不同的方法进行高效处理与分析。为此，本文提出一种异构点云压缩（PCC）框架。我们将点云典型表示形式（基于点、基于体素和基于树的表示）及其对应的主干网络统一到学习框架中，以实现不同比特深度下输入点云的压缩。在认识到体域处理重要性的基础上，我们通过引入上下文感知上采样模块用于解码，并采用增强型体素转换器进行特征聚合来增强该框架。大量实验表明，本方法在多种点云上均达到了最优性能。