近年来,深度传感器和三维扫描仪的普及,使三维点云得到了快速发展。点云语义分割作为三维场景理解和分析的关键步骤,受到了研究者的广泛关注。深度学习具有优良的高层语义理解能力,基于深度学习的点云语义分割已成为当前研究的热点。首先,从语义分割的概念出发,简要叙述了点云语义分割的优势和现存的挑战;进而详细介绍了点云分割算法和常见的数据集,重点对点云语义分割领域中基于点排序、特征融合和图卷积神经网络的深度学习方法进行了综述;最后,分析了所述方法的定量结果,并展望了点云语义分割技术未来的发展趋势。

Over the recent years, the popularity of depth sensors and three-dimensional(3D) scanners has enabled the rapid development of 3D point clouds. As a key step in understanding and analyzing three-dimensional scenes, semantic segmentation of point clouds has received extensive research attention. Point cloud semantic segmentation based on deep learning has become a current research hotspot owing to the excellent high-level semantic understanding ability of deep learning. This paper briefly discusses the concept of semantic segmentation, followed by the advantages and challenges of point cloud semantic segmentation. Then, the point cloud segmentation algorithms and common datasets are introduced in detail. This paper also summarizes the deep learning methods based on point ordering, feature fusion, and graph convolutional neural network in the field of point cloud semantic segmentation. Finally, it analyzes the quantitative results of proposed methods and forecasts the development trend of point cloud semantic segmentation technology in the future.