3D image augmentation using neural style transfer and generative adversarial networks

Abstract

In this paper, we take the very first step in using Neural Style Transfer and Generative Adversarial Networks for the task of 3D image augmentation. With this approach, more data may be generated for object recognition and visualization purposes without having to fully reconstruct 3D objects. To the best of our knowledge, this is the first report that describes image augmentation in the 3D domain using Integral Imaging and Deep Learning. The author assumes that readers have some knowledge regarding Deep Learning.