Learning-Based Rate Control for Learning-Based Point Cloud Geometry Coding

Abstract

Point Clouds represent one of the most versatile 3D visual representation models as they can provide the user the six degrees of freedom required for a truly immersive experience. In the last decade, several point cloud coding solutions have been proposed using distinct approaches, notably two MPEG standards, addressing static and dynamic point cloud coding. More recently, learning-based coding approaches started to be considered also for point cloud coding. The performance of these solutions has been so competitive that JPEG already decided to develop a point cloud coding standard adopting this novel approach. This paper proposes the first learning-based rate control mechanism to minimize the complexity associated to the selection of appropriate coding parameters for the learning-based point cloud geometry codec adopted as the initial Verification Model for the development of the JPEG Pleno Learning-based Point Cloud Coding standard.