  We propose a generative adversarial network for point cloud upsampling, which
can not only make the upsampled points evenly distributed on the underlying
surface but also efficiently generate clean high frequency regions. The
generator of our network includes a dynamic graph hierarchical residual
aggregation unit and a hierarchical residual aggregation unit for point feature
extraction and upsampling, respectively. The former extracts multiscale
point-wise descriptive features, while the latter captures rich feature details
with hierarchical residuals. To generate neat edges, our discriminator uses a
graph filter to extract and retain high frequency points. The generated high
resolution point cloud and corresponding high frequency points help the
discriminator learn the global and high frequency properties of the point
cloud. We also propose an identity distribution loss function to make sure that
the upsampled points remain on the underlying surface of the input low
resolution point cloud. To assess the regularity of the upsampled points in
high frequency regions, we introduce two evaluation metrics. Objective and
subjective results demonstrate that the visual quality of the upsampled point
clouds generated by our method is better than that of the state-of-the-art
methods.
