  Point clouds acquired from range scans are often sparse, noisy, and
non-uniform. This paper presents a new point cloud upsampling network called
PU-GAN, which is formulated based on a generative adversarial network (GAN), to
learn a rich variety of point distributions from the latent space and upsample
points over patches on object surfaces. To realize a working GAN network, we
construct an up-down-up expansion unit in the generator for upsampling point
features with error feedback and self-correction, and formulate a
self-attention unit to enhance the feature integration. Further, we design a
compound loss with adversarial, uniform and reconstruction terms, to encourage
the discriminator to learn more latent patterns and enhance the output point
distribution uniformity. Qualitative and quantitative evaluations demonstrate
the quality of our results over the state-of-the-arts in terms of distribution
uniformity, proximity-to-surface, and 3D reconstruction quality.
