Integral spin images usage in deep learning algorithms for 3D model classification

Abstract

We are investigating a problem of 3D model classification using deep learning algorithms. We propose integral spin images usage as 3D model representation. A number of computational experiments were made to build spin images for 3D models of Princeton Shape Benchmark and use them to train LeNet-5, AlexNet and ResNet deep neural networks. The results showed that integral spin images can be used in conjunction with deep learning algorithms in 3D model classification problem. However, with greater number of classes classification accuracy tends to decrease. It is expected that designing a more complex neural network architecture and expanding number of data characteristics can increase accuracy.