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.

翻译：数字虚拟角色的兴起极大地增加了对具有真实细腻细节的人体点云的需求。然而，包含数百万个点的海量数据使得此类数据的压缩面临挑战。本文利用人体几何先验进行点云几何冗余去除，显著提升了压缩性能。具体而言，该先验作为几何初始化提供拓扑约束，通过仅需极少比特表示的紧凑参数集实现自适应调整。因此，可将高分辨率人体点云视为几何先验与结构偏差的组合。先验可通过对齐点云导出，随后特征差值被压缩为紧凑潜码。所提框架能以即插即用方式与现有基于学习的点云压缩方法协同工作。大量实验结果表明，本方法在保证质量不下降的前提下显著提升了压缩性能，展现出在各类应用中的巨大潜力。