Abstract: The cloud data of metal part surface damage point requires high local feature analysis ability of the segmentation network, and the traditional algorithm with weak local feature analysis ability can not achieve the ideal segmentation effect for the data set. The relative damage volume and other features are selected to classify the metal surface damage, and the damage is divided into six categories. This paper proposes a method to extract the attention feature of 3D map containing spatial scale area information. The obtained spatial scale area feature is used in the design of feature update network module. Based on the feature update module, a feature updated dynamic graph convolution network is constructed for point cloud semantic segmentation. The experimental results show that the proposed method is helpful for more effective point cloud segmentation to extract the local features of point cloud. In metal surface damage segmentation, the accuracy of this method is better than pointnet++, DGCNN(Dynamic Graph Convolutional Neural Networks) and other methods, which improves the accuracy and effectiveness of segmentation results. 

Key words: point cloud segmentation, dynamic graph convolution, feature adaptive shifting, damage classification