  The worldwide commercialization of fifth generation (5G) wireless networks
and the exciting possibilities offered by connected and autonomous vehicles
(CAVs) are pushing toward the deployment of heterogeneous sensors for tracking
dynamic objects in the automotive environment. Among them, Light Detection and
Ranging (LiDAR) sensors are witnessing a surge in popularity as their
application to vehicular networks seem particularly promising. LiDARs can
indeed produce a three-dimensional (3D) mapping of the surrounding environment,
which can be used for object detection, recognition, and topography. These data
are encoded as a point cloud which, when transmitted, may pose significant
challenges to the communication systems as it can easily congest the wireless
channel. Along these lines, this paper investigates how to compress point
clouds in a fast and efficient way. Both 2D- and a 3D-oriented approaches are
considered, and the performance of the corresponding techniques is analyzed in
terms of (de)compression time, efficiency, and quality of the decompressed
frame compared to the original. We demonstrate that, thanks to the matrix form
in which LiDAR frames are saved, compression methods that are typically applied
for 2D images give equivalent results, if not better, than those specifically
designed for 3D point clouds.
