Convolutional neural networks with hybrid weights for 3D point cloud classification

The classification of 3D point clouds is a regular task, but remains a highly challenging problem because 3D point clouds usually contain a large amount of information on irregular shapes. Several recent studies have shown the excellent performance of deep learning in 3D point cloud classification. Convolutional neural network (CNN)-based 3D point cloud classification methods are also increasingly used owing to their efficient and convenient feature extraction capability. However, most of these methods do not take much prior information and local structural information into consideration, often resulting in their inability to extract sufficient information to improve the classification accuracy. In this study, we present a novel convolution operation named HyConv, which includes two key components. First, inspired by 2D convolution, we design a feature transformation module to capture more local structural information. Second, to extract the prior information, a hybrid weight module is introduced to estimate two types of weights on the basis of the distribution information of the spatial and feature domains. Additionally, we propose an adaptive method to learn hybrid weights to obtain hybrid distribution information. Finally, based on the proposed convolutional operator HyConv, we build a deep neural network Hybrid-CNN and conduct experiments on two commonly used datasets. The results show that our hybrid network outperforms most existing methods on ModelNet40. Furthermore, state-of-the-art performance is achieved with ScanObjectNN, which is a great improvement compared with existing methods.