  This paper describes a new approach for training generative adversarial
networks (GAN) to understand the detailed 3D shape of objects. While GANs have
been used in this domain previously, they are notoriously hard to train,
especially for the complex joint data distribution over 3D objects of many
categories and orientations. Our method extends previous work by employing the
Wasserstein distance normalized with gradient penalization as a training
objective. This enables improved generation from the joint object shape
distribution. Our system can also reconstruct 3D shape from 2D images and
perform shape completion from occluded 2.5D range scans. We achieve notable
quantitative improvements in comparison to existing baselines
