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Overview: 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. As a burgeoning format of spatial data, 3D point cloud has triggered a research boom in the field of vision in recent years. It plays an essential role in many modern industrial applications, such as autonomous driving and virtual/augmented reality. To keep up with the ever-growing application requirements and meet the three progressive goals of ease of storage, transmission, and processing, it is of increasing significance to research and develop efficient compression algorithms to deal with the huge amount of point clouds. Point cloud compression technologies have become research hotspots for the past few years. With the development of multimedia technology, 3D vision has aroused extensive research enthusiasm. The basis of 3D vision is to model the exterior surface of a continuous 3D model. Point cloud is a widely used representation of 3D geometric models. Point cloud compression technologies have become an essential branch of 3D computer vision. Point cloud is a collection of discrete sampling points on underlying surfaces embedded in 3D space. Point cloud compression refers to the compression of point cloud data to reduce the occupation of storage space and transmission bandwidth. Point cloud compression technologies employ lossless or lossy compression methods to eliminate redundant information, making it widely applicable in 3D data processing, computer vision, robotics, and other related fields.
Genre: Non-Fiction > Tech & Devices
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