Using GAN to Augment the Synthesizing Images from 3D Models

Annotation data is the “fuel” of vision cognitive system but hard to obtain. We focus on finding a feasible way to generate high-quality image data. The 3D models can produce rich annotated 2D images, and the generative adversarial nets can create various pictures. We proposed the background augmentation generative adversarial nets to build a bridge between GAN and 3D models for data augmentation. As a result, we use BAGAN and 3D models to generate images which can help deep convolutional classifier improve accuracy score to 93.12% on real data test sets.