Abstract: The classification and semantic segmentation of point cloud data have important applications in automatic driving,intelligent robot and holographic projection.While using the traditional method of manually extracting point cloud features or the feature learning method of firstly transforming three-dimensional point cloud data into data forms of multi-view and volumetric grid,there exist problems such as many processing links and great loss of three-dimensional features,resulting in low accuracy of classification and segmentation.The existing deep neural network PointNet,which can directly process point cloud data,ignoresthe local fine-grained features of point cloud and is weak in processing complex point cloud scenarios.To solve the above problems,this paper proposes a point cloud deep learning network based on dynamic graph convolution and spatial pyramid pooling.On the basis of PointNet,the dynamic graph convolution module GraphConv is used to replace the feature learning module in PointNet,which enhances the network’s ability to learn local topological structure information.At the same time,a point-based spatial py-ramid pooling structure PSPP is designed to capture multi-scale local features.Compared with the multi-scale sampling point cloud of PointNet++ and the repeated grouping method for multi-scale local features learning,it is simpler and more efficient.Experimental results show that,on the three benchmark data sets of point cloud classification and semantic segmentation task,the proposed network has higher classification and segmentation accuracy than the existing network.

Key words: Dynamic graph convolution, Local features, Point cloud, PointNet, Spatial pyramid pooling