SSA-Point Net++:A Space Self-Attention CNN for the Semantic Segmentation of 3D Point Cloud

To improve the accuracy of semantic segmentation of point cloud in complex scenes,a novel convolutional neural network(CNN)called SSA-PointNet++by imposing the self-attention mechanism onto traditional PointNet++network is proposed.Firstly,the self attention of the neighborhood of the sampling point is divided into two parts:the central self-attention and the neighborhood-attention.Two kinds of self-attention mechanism are then combined to improve the network’s ability in capturing fine-grained local features.Secondly,one attention pooling module is constructed based on adaptive selection of features from the attention mechanism for the effective transmission of important information in the network.The global features extracted from the attention pooling and the maximum pooling is effectively fused to improve the robustness of the point cloud semantic segmentation results.Experiments on public data sets S3 DIS and Semantic3 D show that the proposed network outperforms are improved compared with the benchmark model in terms of both the overall accuracy and mIoU.For indoor dataset S3 DIS,the mIoU of the proposed SSA-PointNet++is good and 6.6%higher than PointNet++.For outdoor dataset Semantic3 D,the mIoU of the proposed SSA-PointNet++is about 3%higher than MSDeepVoxNet.Compared with the segmentation results of other networks on the public datasets,proposed network is more general on different datasets and high application potential.