3D point cloud compression: A survey

Abstract

In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data. For a typical example of a point cloud with 0.7 million points per 3D frame at 30 fps, the point cloud raw video needs a bandwidth around 500MB/s. Thus, efficient compression methods are mandatory for ensuring the storage/transmission of such data, which include both geometry and attribute information. In the last years, the issue of 3D point cloud compression (3D-PCC) has emerged as a new field of research. In addition, an ISO/MPEG standardization process on 3D-PCC is currently on-going. In this paper, a comprehensive overview of the 3D-PCC state-of-the-art methods is proposed. Different families of approaches are identified, described in details and summarized, including 1D traversal compression, 2D-oriented techniques, which take leverage of existing 2D image/video compression technologies and finally purely 3D approaches, based on a direct analysis of the 3D data.