Cluster-based point cloud attribute compression using inter prediction and graph Fourier transform

Abstract

With the rapid development of 3D capture technologies, point cloud has been widely used in many emerging applications such as augmented reality, autonomous driving, and 3D printing. However, point cloud, used to represent real world objects in these applications, may contain millions of points, which results in huge data volume. Therefore, efficient compression algorithms are essential for point cloud when it comes to storage and real-time transmission issues. Specially, the attribute compression of point cloud is still challenging owing to the sparsity and irregular distribution of corresponding points in 3D space. In this paper, we present a novel point cloud attribute compression scheme based on inter-prediction of blocks and graph Laplacian transforms for attributes residual. Firstly, we divide the entire point cloud into adaptive sub-clouds via K-means based on the geometry to acquire sub-clouds, which enables efficient representation with less cost. Secondly, the sub-clouds are divided into two parts, one is the attribute means of the sub clouds, another is the attribute residual by removing the means. For the attribute means, we use inter-prediction between sub-clouds to remove the attribute redundancy, and the attribute residual is encoded after graph Fourier transforming. Experimental results demonstrate that the proposed scheme is much more efficient than traditional attribute compression schemes.