Abstract: A semantic segmentation method integrating block features is proposed to reduce the computational complexity of semantic segmentation of multi-class 3D objects in outdoor large-scale point cloud scenes.The square grid segmentation method is used to divide, sample, and combine the 3D point cloud, obtain the simplified point cloud combination block set, and input it into the block feature extraction and fusion network, to obtain the feature correction vector of each block.In order to correct the wrong feature caused by segmentation, the global feature correction network of the point cloud block is designed, and the feature correction vector is fused with the global feature of the original point cloud in the form of a residual.Based on this, the square mesh segmentation size is introduced into the back-propagation process as a parameter of the neural network for optimization, to establish an efficient point cloud semantic segmentation network.The experimental results show that the back propagation algorithm can optimize the segmentation size to near the optimal value, and the global feature correction method in the proposed network can improve the semantic segmentation accuracy.The semantic segmentation accuracy of this method on the Semantic3D dataset is 78.7%, which is 1.3% higher than the RandLA-Net method, and its point cloud preprocessing computational complexity and network computing time are significantly reduced on the premise of ensuring segmentation accuracy.When processing large-scale point clouds with 100 000‒1 million points, the semantic segmentation speed of point clouds is 2‒4 times higher than that of SPG, KPConv, and other methods.
point cloud semantic segmentation,
block feature fusion,
point cloud feature extraction,
point cloud preprocessing