3D Shape Classification Using a Single View

Abstract

View-based 3D shape classification is widely used in machine vision, information retrieval and other fields. However, there are two problems in current methods. First, current 3D shape classifiers fail to make good use of pose information of 3D shapes. Secondly, many views are required to obtain good classification accuracy, which leads to low efficiency. In order to solve these problems, we propose a novel 3D shape classification method based on Convolutional Neural Network (CNN). In the training stage, this method first learns a CNN to extract features, and then uses features of views from different viewpoint groups to train six 3D shape classifiers which fully mine the pose information of 3D shapes. Meanwhile, an additional class is adopted to improve the discrimination of 3D shape classifiers. In the recognition stage, the weighted fusion of image clarity evaluation functions is used to select the most representative view for the 3D shape recognition. Experiments on the ModelNet10 and ModelNet40 show that the classification accuracy of the proposed method can reach up to 91.18% and 89.01% when only using a single view and the efficiency is improved substantially.