3D-GANWu, Jiajun, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. "Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling." Advances in neural information processing systems 29 (2016). was designed to generate a 3D point cloud of certain types of objects. The design and training process of 3D-GAN is very similar to the vanilla GAN, except that the input and output tensors of the 3D-GAN are five-dimensional rather than four-dimensional.
Generators and discriminators in 3D-GAN
The architecture of the generator network of 3D-GAN is as follows: