3D point cloud segmentation: A survey

Abstract

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation. Many authors have introduced different approaches and algorithms. In this survey, we examine methods that have been proposed to segment 3D point clouds. The advantages, disadvantages, and design mechanisms of these methods are analyzed and discussed. Finally, we outline the promising future research directions.