Abstract: 3D models enjoy a popularity. It has always been our concern as to how to effectively manage and classify the 3D models in these databases. However, due to the similarity between different 3D models is difficult to calculate, it is difficult to obtain a robust and widely applicable 3D model classification algorithm. Thus a weighted optimization integrated convolutional neural network model is proposed and applied to the classification and recognition of 3D models. Firstly, the depth projection view of the 3D model is obtained to maximize the reserve of spatial information of the 3D model. Then, the adjusted VGG network is used to train the depth projection images from different angles and extract the predictive probability values. Finally, the final classification results of the complete 3D model are obtained by weighted ensemble algorithm. The experiments on ModelNet10 and ModelNet40 databases show that the average classification accuracy of the 3D model is 92.84% and 86.51% respectively. In terms of performance prediction, the network is superior to the ordinary single convolution neural network, and its classification accuracy can be significantly improved in 3D model recognition.

Key words:  3D model classification, voxelization, convolutional neural network, ensemble learning, weighted optimization