A Robust Deep Neural Network for Multi-Feature Point Cloud Classification and Segmentation

Abstract

The existing deep learning-based methods for point cloud classification and segmentation usually fail to learn the local features of point clouds,which limits their accuracy and robustness.To address the problem,a robust deep neural network,RMFP-DNN,is proposed for multi-feature point cloud classification and segmentation.The network employs a self-attention module to extract the local features of point clouds,and uses the Multi-Layer Perceptron(MLP) to learn the global features of point clouds.On this basis,the extracted local and global features are fused to improve the accuracy and robustness of classification and segmentation.Experimental results show that the average classification accuracy and overall classification accuracy of RMFP-DNN are 88.9% and 92.6% respectively.Compared with PointNet,PointNet++and DGCNN,RMFP-DNN achieves higher accuracy and better robustness.