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.

翻译：点云流媒体日益普及，正逐渐成为交互式服务交付和未来元宇宙的常态。然而，点云数据量庞大，在带宽消耗和存储容量方面带来诸多挑战。尽管现有研究已提出多种解决方案，主要聚焦于点云压缩、上采样和补全，但这些重建相关方法仍难以输出高保真点云。为此，我们在DiffPMAE中提出一种有效的点云重建架构。受自监督学习理念启发，我们融合掩码自编码与扩散模型机制，对点云数据进行远程重建。凭借重建过程的特性，DiffPMAE可扩展至点云压缩、上采样和补全等相关下游任务。基于包含超过6万个对象的ShapeNet-55和ModelNet数据集，我们验证了DiffPMAE在自编码性能及所涉及下游任务中均超越了多种前沿方法。