Abstract: As point clouds are widely utilized in various fields such as autonomous driving,map making,and mining measurement,there is a growing interest in this data representation that contains rich information.Point cloud semantic segmentation,as an important means of point cloud data processing,has attracted wide attention due to its high research value and application prospects.Due to the characteristics of permutation invariance and rotation invariance in point clouds,traditional convolutional neural networks cannot directly process irregular point cloud data,but graph convolutional neural networks can use graph convolution operators to directly extract point cloud features.Therefore,this paper provides a detailed review of recent point cloud segmentation methods based on graph convolution.The methods are further divided according to the type of graph convolution,and representative algorithms in each category are introduced and analyzed,summarizing the research ideas and advantages and disadvantages of each method.Then,some mainstream point cloud datasets and evaluation metrics in the field of point cloud semantic segmentation are introduced,and the experimental results of the mentioned segmentation methods are compared.Finally,the development direction of various methods is discussed.

Key words: Semantic segmentation, Point clouds, Graph convolution neural network, Deep learning, Computer vision