  Point cloud streaming is increasingly getting popular, evolving into the norm
for interactive service delivery and the future Metaverse. However, the
substantial volume of data associated with point clouds presents numerous
challenges, particularly in terms of high bandwidth consumption and large
storage capacity. Despite various solutions proposed thus far, with a focus on
point cloud compression, upsampling, and completion, these
reconstruction-related methods continue to fall short in delivering high
fidelity point cloud output. As a solution, in DiffPMAE, we propose an
effective point cloud reconstruction architecture. Inspired by self-supervised
learning concepts, we combine Masked Auto-Encoding and Diffusion Model
mechanism to remotely reconstruct point cloud data. By the nature of this
reconstruction process, DiffPMAE can be extended to many related downstream
tasks including point cloud compression, upsampling and completion. Leveraging
ShapeNet-55 and ModelNet datasets with over 60000 objects, we validate the
performance of DiffPMAE exceeding many state-of-the-art methods in-terms of
auto-encoding and downstream tasks considered.
