Abstract
Abstract 3D point cloud segmentation is one of the key steps in point cloud processing, which is the technology and process of dividing the point cloud data set into several specific regions with unique properties and proposing interesting targets. It has important applications in medical image processing, industrial inspection, cultural relic’s identification and 3D visualization. Despite widespread use, point cloud segmentation still faces many challenges because of uneven sampling density, high redundancy, and lack explicit structure of point cloud data. The main goal of this paper is to analyse the most popular algorithms and methodologies to segment point clouds. To facilitate analysis and summary, according to the principle of segmentation we divide the 3D point cloud segmentation methods into edge-based methods, region-based methods, graph-based methods, model-based methods, and machine learning-based methods. Then analyze and discuss the advantages, disadvantages and application scenarios of these segmentation methods. For some algorithms the results of the segmentation and classification is shown. Finally, we outline the issues that need to be addressed and important future research directions.
Highlights
Image segmentation is one of the basic research directions of computer vision, and its purpose is to subdivide a digital image into multiple regions with similar properties[1]
Compared with several segmentation methods, the results show that this method is suitable for segmenting foreground and background, suitable for specified target extraction, or implementing multi-objective extraction in a supervised classification manner
Ability to realize the automatic classification of LiDAR point clouds in complex scenes; Niu[36] improved the problem of the lack of local topological information of the generated features, and proposed a method that uses bisymmetric functions and spatial transformation networks to obtain more robust and stronger discrimination Compared with PointNet++, the training time is reduced by 20% with the same accuracy
Summary
Image segmentation is one of the basic research directions of computer vision, and its purpose is to subdivide a digital image into multiple regions with similar properties[1]. Segmentation of 2D images has more than 50 years of research history, but 3D point cloud data is a highly redundant and irregularly ordered structure, point cloud segmentation faces many challenges. The segmentation of point clouds into foreground and background is a fundamental step in processing 3D point clouds. Given the set of point clouds, the 3D point cloud segmentation can be defined with the sets: 1) n Ri S. Indicates that the union of the divi-ded i 1 regions is the measured point set S , that is, each measurement point is divided into a certain region. 2) Ri Rj Ø, indicates that the point sets obtained by segmentation do not intersect each other, and each measurement point cannot belong to two different regions at the same time.
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