Abstract
With the rapid development of 3D acquisition technology, 3D point cloud data has become a fast and accurate means to obtain 3D spatial information. However, as a kind of non-Euclidean data, the point cloud's disorder and sparseness make it difficult to train directly using conventional CNN. We propose a 3D point cloud semantic segmentation network based on graph convolution and U-shaped network structure. Among them, the graph convolution module with attention mechanism is used to aggregate the local features of the point cloud data. The U-shaped network with random sampling is used for semantic segmentation to make the network more accurate and more robust. Finally, we evaluated our model on S3DIS indoor data set and got competitive results compared with the benchmark method.
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