Indoor point cloud semantic segmentation based on direction perception and hole sampling

Most existing point cloud segmentation methods ignore directional information when extracting neighbourhood features. Those methods are ineffective in extracting point cloud neighbourhood features because the point cloud data is not uniformly distributed and is restricted by the size of the convolution kernel. Therefore, we take into account both multiple directions and hole sampling (MDHS). First, we execute spherically sparse sampling with directional encoding in the surrounding domain for every point inside the data to increase the local perceptual field. The data input is the basic geometric features. We use the graph convolutional neural network to conduct the maximisation of point cloud characteristics in a local neighbourhood. Then the more representative local point features are automatically weighted and fused by an attention pooling layer. Finally, spatial attention is added to increase the connection between remote points, and then the segmentation accuracy is improved. Experimental results show that the OA and mIoU are 1.3% and 4.0% higher than the method PointWeb and 0.6% and 0.7% higher than the baseline method RandLA-Net. For the indoor point cloud semantic segmentation, the segmentation effect of the proposed network is superior to other methods.