A novel method for classification of 3D point clouds based on the improved PointNet++

Abstract In the field of deep learning, point clouds are used as the basic input format for 3D data, which can provide detailed geometric information about objects in the original 3D space. PointNet + + is a deep learning network that uses point cloud data as an input format, which avoids the losses associated with the previous conversion of point cloud into 3D voxelization as well as a collection of 2D images. Although PointNet + + can directly process point cloud data in various ways, due to the disordered, irregular and unevenly distributed nature of point cloud data, the effect of extracting point cloud features is not ideal, and due to the large amount of point cloud data, it also leads to the training model falling into the local optimal solution, which affects the training results. To address these problems, some effective methods and strategies have emerged in recent years. In this thesis, three methods are proposed on the basis of PointNet + + network: feature similarity-based attention pooling, adaptive regularization term and fixed random seed method to improve the performance of PointNet + + network. Experiments show that the improvement methods proposed in this paper effectively improve the feature extraction accuracy, which in turn improves the accuracy of PointNet + + network for classification on Modelnet40 dataset, with an overall improvement of 0.68% compared with PointNet++.