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.

点云流媒体成为交互式服务交付和未来元宇宙的常态，但庞大的数据量带来了诸多挑战，特别是高带宽消耗和大储存容量。为解决这些问题，我们提出了一种有效的点云重建架构 DiffPMAE，通过自监督学习和扩散模型机制远程重建点云数据，并在点云压缩、上采样和补全等下游任务中验证了其在自动编码和相关任务方面的优越性。