A Comparison Study Of Point Cloud Compression Algorithms
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Point Cloud Compression
3D point clouds have broad applications across various industries and have contributed to advancements in fields such as autonomous driving, immersive media, metaverse, and cultural heritage protection. With the fast growth of 3D point cloud data and its applications, the need for efficient compression technologies has become paramount. This book delves into the forefront of point cloud compression, exploring key technologies, standardization efforts, and future prospects. This comprehensive book uncovers the foundational concepts, data acquisition methods, and datasets associated with point cloud compression. By examining the fundamental compression technologies, readers can obtain a clear understanding of prediction coding, transform coding, quantization techniques, and entropy coding. Through vivid illustrations and examples, the book elucidates how these techniques have evolved over the years and their potentials for the future. To provide a complete picture, the book presents cutting-edge research methods in point cloud compression and facilitates comparisons among them. Readers can be equipped with an in-depth understanding of the latest advancements, and can gain insights into the various approaches employed in this dynamic field. Another distinguishing aspect of this book is its exploration of standardization works for point cloud compression. Notable standards, such as MPEG G-PCC, AVS PCC, and MPEG V-PCC, are thoroughly illustrated. By delving into the methods used in geometry-based, video-based, and deep learning-based compression, readers become familiar with the latest breakthroughs in the standard communities.
A Comparison Study of Point Cloud Compression Algorithms
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.
Proceedings of International Conference on Image, Vision and Intelligent Systems 2024 (ICIVIS 2024)
This book constitutes the refereed proceedings of ICIVIS2024, held in Xining, China, in June 2024. This book provides a comprehensive collection of cutting-edge research and innovative solutions in image, vision and intelligent systems. The primary audience consists of academic researchers, industry professionals, and graduate students working in the domains of image, vision, and intelligent systems. This publication serves as an essential resource for those seeking to stay at the forefront of their respective fields, expand their knowledge, and explore new avenues for research and development.