Point cloud is a kind of widely used 3D data, and semantic segmentation, as a key technology for 3D scene understanding, is increasingly in demand. In the past three years, point cloud semantic segmentation technology has been developing rapidly, and in order to show the progress in deep learning-based 3D point cloud semantic segmentation for indoor scenes, the latest research trends in the past three years are highlighted. Firstly, we introduce the commonly used datasets and evaluation indexes for point cloud semantic segmentation, then we classify the various point cloud semantic segmentation methods in the past three years, analyze and summarize the framework structure of the methods and their innovations according to different categories from the perspective of indirectly and directly dealing with point clouds, and compare and contrast the evaluation indexes of the various algorithms on several most commonly used indoor datasets, such as S3DIS, ScanNet, etc., such as the mIou indexes. metrics are compared and demonstrated. Finally, the current research status and existing problems of semantic segmentation techniques for point clouds are summarized and outlooked.