  The reconstruction of real-world surfaces is on high demand in various
applications. Most existing reconstruction approaches apply 3D scanners for
creating point clouds which are generally sparse and of low density. These
points clouds will be triangulated and used for visualization in combination
with surface normals estimated by geometrical approaches. However, the quality
of the reconstruction depends on the density of the point cloud and the
estimation of the surface normals. In this paper, we present a novel deep
learning architecture for point cloud upsampling that enables subsequent stable
and smooth surface reconstruction. A noisy point cloud of low density with
corresponding point normals is used to estimate a point cloud with higher
density and appendant point normals. To this end, we propose a compound loss
function that encourages the network to estimate points that lie on a surface
including normals accurately predicting the orientation of the surface. Our
results show the benefit of estimating normals together with point positions.
The resulting point cloud is smoother, more complete, and the final surface
reconstruction is much closer to ground truth.
