Temporal Conditional Coding for Dynamic Point Cloud Geometry Compression

Abstract

Point clouds allow for the representation of 3D multimedia content as a set of disconnected points in space. Their inherent irregular geometric nature poses a challenge to efficient compression, a critical operation for both storage and transmission. This paper proposes a VAE-inspired codec tailored for dynamic point cloud geometry compression, taking advantage of a temporal autoregressive hyperprior to enhance compression performance. Specifically, features derived from adjacent point cloud frames help build a hyperprior for conditional entropy coding. Sparse convolutions are leveraged to reach higher computational efficiency when compared to 3D dense convolutions. Remarkably, the proposed approach achieves an average 60.2% BD-rate gain against the contemporary V-PCC compression standard from MPEG.