Introduction

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. In this chapter, we first introduce the basic concepts of 3D point clouds and the current mainstream point cloud data acquisition methods. After that, we present several public point cloud benchmark datasets and specific application scenarios of point cloud compression techniques. We further elaborate on the development trends and challenges in point cloud technologies.