Optimized PointNet for 3D Object Classification

Abstract

Three-dimensional (3D) laser scanning technology is widely used to get the 3D geometric information of the surrounding environment, which leads to a huge increase interest of point cloud. The PointNet based on neural network can directly process point clouds, and it provides a unified frame to handle the task of object classification, part segmentation and semantic segmentation. It is indicated that the PointNet is efficient for target segmentation. However, the number of neural network layers and loss function are not good enough for target classification. In this paper, we optimize the original neural network by deepen the layers of neural network. Simulation result shows that the overall accuracy increases from 89.20% to 89.35%. Meanwhile, the combination of softmax loss with center loss function is adopt to enhance the robustness of classification, and the overall accuracy is up to 89.95%.