3D-GANs

3D-GANs were proposed by Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum in their paper titled Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling, which is available at the following link: https://arxiv.org/pdf/1610.07584. Generating 3D models of objects has many use cases in manufacturing and the 3D modeling industry. A 3D-GAN network is able to generate new 3D models of different objects, once trained on 3D models of objects. We will learn how to generate 3D models of objects using a 3D-GAN in Chapter 2, 3D-GAN - Generating Shapes Using GAN.