Point clouds (PCs) are an essential data format for the transmission and storage of immersive visual content and 3D visual data. They are employed in a wide spectrum of applications, including virtual reality [10], mixed reality [19], autonomous driving [78], construction [72], cultural heritage [64], etc. Point clouds represent 3D data as a set of points with (x, y, z) coordinates, also referred to as the point cloud geometry, and associated attributes, such as colors, normals and reflectance. Furthermore, depending on whether the PC includes a temporal dimension, we can further distinguish between static and dynamic point clouds. The number of points in a PC can easily range in the order of millions, and can feature complex attributes. As a result, the bitrate required to transmit raw (uncompressed) point clouds largely exceeds the available bandwidth in many communication systems. For example, a dynamic point cloud, such as the one depicted in Fig. 13.1 (a), contains around one million points per frame, and is acquired at 30 frames per second. The original PC geometry, obtained, eg, through photogrammetry (ie, by estimating the disparity of objects across an array or rig of calibrated cameras), is typically represented with a floating point number (32 bits) per coordinate, to which we need to add the color information (typically 8 bits per channel). This results in a required bandwidth without compression of 3.6 Gbps. Another example comes from LiDAR scans (see Fig. 13.1 (c)). A typical LiDAR sensor, such as the Velodyne HDL-64, 1 typically used for autonomous driving applications can acquire over 100k points per sweep, yielding 3 billion points …