Abstract: Vehicle-mounted LiDAR point cloud semantic annotation is a prerequisite for further semantic analysis and understanding of road scenes. This paper proposes a point cloud classification method that integrates segmentation algorithm and graph convolution network. First, point cloud is segmented into point clusters using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Point clusters are treated as nodes, with adjacent clusters forming edges, thus constituting a graph. Then, graph convolution network is used to semi-supervise the classification of graph nodes and obtain the semantic annotation of any point in the point cloud. Experimental results demonstrate that replacing the original point cloud with point clusters greatly reduces the amount of data processed by the algorithm. Furthermore, the semi-supervised graph convolution network, considering the context of point cloud, achieves high classification accuracy even with a small number of labeled samples. The classification accuracy of experimental data with simple scenes is comparable to that of pointnet++, with the difference in accuracy below 6.7% for complex scenes.

Key words: point cloud segmentation, graph convolution network, point cloud classification