Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

arXiv:2303.06519v2 Announce Type: replace-cross
Abstract: In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three …

abstract arxiv autonomous autonomous driving cloud cloud data compression cost cs.lg data driving eess.iv geometry healthcare immersive life media paper probability type