Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability

Point clouds are a 3D visual representation format that has recently become fundamentally important for immersive and interactive multimedia applications. Considering the high number of points of practically relevant point clouds, and their increasing market demand, efficient point cloud coding has become a vital research topic. In addition, scalability is an important feature for point cloud coding, especially for real-time applications, where the fast and rate efficient access to a decoded point cloud is important; however, this issue is still rather unexplored in the literature. In this context, this paper proposes a novel deep learning-based point cloud geometry coding solution with resolution scalability via interlaced sub-sampling. As additional layers are decoded, the number of points in the reconstructed point cloud increases as well as the overall quality. Experimental results show that the proposed scalable point cloud geometry coding solution outperforms the recent MPEG Geometry-based Point Cloud Compression standard which is much less scalable.