曲线和多头移动通道自注意力机制融合的点云语义分割OA

In order to solve the problem of inadequate extraction of local spatial structure and deep-level point cloud features in point cloud semantic segmentation.We proposed a 3D point cloud semantic segmentation network based on the fusion of curve and multi-head shifted channel self-attention mechanism.First,the curve module performed grouping and walking operations on the point cloud through a dynamic walking strategy to obtain the correlation and geometric corre-lation between remote points.Secondly,the multi-head shifted channel self-attention mechanism module was introduced to segment the channels by sliding windows and construct multi-head self-attention aggregated channel features to capture the deep semantic information of the point cloud.Finally,the reverse bottleneck module was proposed to deepen the hierarchy of the network by embedding low-dimensional MLP into the interpolation structure to enhance the expression of the features,and at the same time to effectively improve to the gradient vanishing and overfitting problems.The experimental results show that the accuracy of this paper's model is 90.1%and the mean intersection over union is 68.6%on the S3DIS Area 5 dataset;the mean intersection o-ver union used for testing in ScanNet is 70.9%.