Research on 3D Point Cloud Classification Based on Density-Based Spatial Clustering of Algorithm with Noise

The classification of three-dimensional point clouds is a complex task because of its disorder and uneven density. This paper proposes that in the point-cloud preprocessing stage, the Density-Based Spatial Clustering of Algorithm with Noise (DBSCAN) is added to cluster the three-dimensional point cloud, then the clustering results are extracted through the PointNet deep learning network to extract the characteristics of the local area, thus outputting the classification results of the point cloud. This method can not only reflect the feature distribution of point cloud in three-dimensional space, but also can be divided into several classes according to the different shape features of point cloud. Verified in the ModelNet10 and ModelNet40 point cloud dataset, the classification accuracy of this method on both ModelNet10 and ModelNet40 can reach more than 92.5%.