Integrated learning-based point cloud compression for geometry and color with graph Fourier transforms

Point cloud representation is a popular modality to code immersive 3D contents. Several solutions and standards have been recently proposed in order to efficiently compress the large volume of data that point clouds require, in order to make them feasible for real-life applications. Recent studies adopting learning-based methods for point cloud compression have demonstrated high compression efficiency specially when compared to the conventional compression standards. However, they are mostly evaluated either on geometry or color separately, and no learning-based joint codec with performance comparable to state-of-the-art methods have been proposed. In this paper, we propose an integrated learned coding architecture by joining a previously proposed geometry coding module based on three-dimensional convolutional layers with a color compression method relying on graph Fourier transform (GFT) using a learning-based mean and scale hyperprior to compress the obtained coefficients. Evaluation on a test set with dense point clouds shows that the proposed method outperforms GPCC and achieves competitive performance with V-PCC when evaluated with state-of-the-art objective quality metrics