The emergence of digital avatars has raised an exponential increase in the
demand for human point clouds with realistic and intricate details. The
compression of such data becomes challenging with overwhelming data amounts
comprising millions of points. Herein, we leverage the human geometric prior in
geometry redundancy removal of point clouds, greatly promoting the compression
performance. More specifically, the prior provides topological constraints as
geometry initialization, allowing adaptive adjustments with a compact parameter
set that could be represented with only a few bits. Therefore, we can envisage
high-resolution human point clouds as a combination of geometric priors and
structural deviations. The priors could first be derived with an aligned point
cloud, and subsequently the difference of features is compressed into a compact
latent code. The proposed framework can operate in a play-and-plug fashion with
existing learning based point cloud compression methods. Extensive experimental
results show that our approach significantly improves the compression
performance without deteriorating the quality, demonstrating its promise in a
variety of applications.

本文提出了一种基于人体几何先验知识的点云压缩框架，通过减少几何冗余以及提供拓扑学约束，实现了对高分辨率的人体点云数据的压缩，该方法在不降低数据质量的情况下，显著提高了压缩性能。