  Learning and analyzing 3D point clouds with deep networks is challenging due
to the sparseness and irregularity of the data. In this paper, we present a
data-driven point cloud upsampling technique. The key idea is to learn
multi-level features per point and expand the point set via a multi-branch
convolution unit implicitly in feature space. The expanded feature is then
split to a multitude of features, which are then reconstructed to an upsampled
point set. Our network is applied at a patch-level, with a joint loss function
that encourages the upsampled points to remain on the underlying surface with a
uniform distribution. We conduct various experiments using synthesis and scan
data to evaluate our method and demonstrate its superiority over some baseline
methods and an optimization-based method. Results show that our upsampled
points have better uniformity and are located closer to the underlying
surfaces.
