  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.
