2022/02/01 by Francesco Nardo, Davide Peressoni, Nardo, Francesco +8 · 2 citations
Computer Science · Engineering · Environmental Science · #3D Shape Modeling and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Networking and Internet Architecture (cs.NI) #Remote Sensing and LiDAR Applications #Robotics and Sensor-Based Localization #cs.NI #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2202.00719
6 pages, 5 figures, 1 table. This paper has been accepted for presentation at IEEE Wireless Communications and Networking Conference (WCNC) 2022. Please cite it as: F. Nardo, D. Peressoni, P. Testolina, M. Giordani, and A. Zanella, "Point cloud compression for efficient data broadcasting: A performance comparison," IEEE Wireless Communications and Networking Conference (WCNC), Austin, USA, 2022
arxiv created 2022/02/01 · openalex publication_date 2022/02/01 · arxiv updated 2022/02/03 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.