Residual Convolution Network Optimization for View Features Extraction of 3D Model

On the basis of the existing residual convolutional neural networks, the weighted loss function is used to improve the discriminability of the view features of 3D models. A new view feature extraction algorithm of 3D models is proposed to optimize the residual convolutional networks. Firstly, a 3D model is rendered to obtain different views. Then, a residual network expansion module is used to increase depth of the network. Meanwhile, a weighted loss function is defined by combining the center loss function and the cross entropy loss function. As a result, it can solve the problem that the intra-class distance is less than the inter-class distance. Experiments on ModelNet datasets show that the algorithm’s performance is excellent in 3D model classification.