Multi-scale Information Extraction Residual Network for 3D Point Clouds Classification

Abstract

3D Point clouds are irregular and unordered. Recently, neural networks applying on point clouds have shown superior performance on 3D object classification and segmentation. PointNet has achieved competitive performance in classification tasks, however, it ignores the important local information of point clouds, which carries the important information in 3D geometric space. The informations lack will reduce the accuracy of object classification. In this paper, a new deep neural network model is proposed. Specifically, a multiscale network is used to extract local features of points to improve the accuracy of PointNet and a deep residual network is used to avoid the vanishing gradient problem in the process of deep training network. Experiments show that the proposed network architecture achieves better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.