A Comparison Study Of Point Cloud Compression Algorithms

Download or read book A Comparison Study of Point Cloud Compression Algorithms written by Mai P. Bui and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Three-dimensional (3D) sensors, such as Light Detection and Ranging (LiDAR), stereo cameras, and radar have many applications, e.g., virtual/augmented reality (VR/AR), real-time immersive communications, and autonomous driving guidance system. The output of 3D sensors is generally represented in the form of point clouds. A point cloud consists of a set of data points. Each point has its coordinates (X, Y, Z) and associated attributes such as color in Red, Green, Blue (RGB) values. However, the volume of point cloud data generated by 3D sensors is massive. Generating a huge amount of data from point cloud addresses the storing and transmitting challenge: store the point cloud data locally on a device, share the data with other network nodes (i.e., transmit the data in wireless networks), or to manipulate and analyze the data. Therefore, effective compression schemes are needed for reducing the bandwidth of wireless networks or storage space of 3D point cloud data. This thesis aims to develop an efficient 3D point cloud stream compression benchmark that utilities several state-of-the-art 3D point clouds compression (PCC) techniques. In this study, we investigate five state-of-the-art PCC methods using five different datasets with various configurations. The objective of this study is to provide a comprehensive understanding of various approaches in PCC. The results of this paper will be helpful in developing an adaptive 3D point cloud stream compression benchmark that is efficient and benefited from different PCC techniques.