Augmented Normalizing Flow for Point Cloud Geometry Coding

Abstract

With the increased popularity of immersive media, point clouds have become one of the popular data representations for presenting 3D scenes. The huge amount of point cloud data poses a great challenge on their storage and real-time transmission, which calls for efficient point cloud compression. This paper presents a novel point cloud geometry compression technique based on learning end-to-end an augmented normalizing flow (ANF) model to represent the occupancy status of voxelized data points. The higher expressive power of ANF than variational autoencoders (V AE) is leveraged for the first time to represent binary occupancy status. Compared to two coding standards developed by MPEG, namely G-PCC (geometry-based point cloud compression) and V-PCC (video-based point cloud compression), our method achieves more than 80% and 30% bitrate reduction, respectively. Compared to several learning-based methods, our method also yields better performance.