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.

该研究提出了一个异构点云压缩框架，统一了点基、体素基和树基三种典型点云表示及其相关骨干结构，以在不同位深水平对输入点云进行压缩。实验证明了该提议在各种点云上的最新性能。

PIVOT-Net: 基于异构点-体素树的点云压缩框架