| Summary: | In recent years, the popularity of depth sensors and 3D laserscanners has led to a rapid development of 3D point clouds processing methods. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. With the rapid development of deep learning and its widespread applications in 3D semantic segmentation, the quality of point cloud semantic segmentation has been significantly improved. This paper mainly reviews the deep learning-based point cloud semantic segmentation methods and their research status. This paper categories these deep learning-based methods for point clouds into two groups: indirect and direct semantic segmentation methods. In terms of the characteristics of the algorithm, each of groups is further subdivided. The representative algorithms are analyzed and introduced. This paper summarizes the theories, principles, advantages and disadvantages of each type of method, and systematically explains the contribution of deep learning to the field of semantic segmentation. Moreover, the current mainstream datasets and remote sensing datasets are summarized and the experimental results of some algorithms are compared. Finally, the future development direction of semantic segmentation technology is prospected.
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