Abstract:To address the deficiencies of traditional graph neural network methods in point cloud semantic segmentation,such as high requirements for supervision accuracy,one-way only node label propagation,and neglect of global information,this paper proposes a point cloud semantic segmentation method based on bidirectional attention mechanism.Firstly,the point cloud is over-segmented into superpoints and a superpoint graph is constructed,thus introducing the point cloud classification problem into the superpoint graph network framework.Subsequently,the two-way attention module is utilized to alternately focus on superpoints and update their features according to the weights of neighboring superpoints,enabling the two-way information propagation.Unlike previous graph pooling methods,this study applies both maximum pooling and average pooling,and combines their pooled features.Finally,the public dataset Semantic 3D is used for training and experiments.The results show that the proposed method can effectively correct labelling errors while coupling local features with long-range information,and the mean Intersection over Union (mIoU) and overall Accuracy (oAcc) of the dataset are 75.4% and 95.1%,respectively,exhibiting a better label delivery mechanism and higher classification accuracy compared with existing methods.