Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison

Abstract

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 threedimensional (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.

Lidar Point Cloud Compression, Processing and Learning for Autonomous Driving

“…Local motion information may be used to and Lempel-Ziv-Welch (LZW) [96], [98]. The most promising schemes [97] provide accurate but efficient compression for point clouds. A point cloud's Peak signal to Noice Ratio (PSNR) and decompression time are two important features that determine how well the compressed point cloud visual aspects and how well it can be decompressed in a short amount of time.…”