  The demand for high-resolution point clouds has increased throughout the last
years. However, capturing high-resolution point clouds is expensive and thus,
frequently replaced by upsampling of low-resolution data. Most state-of-the-art
methods are either restricted to a rastered grid, incorporate normal vectors,
or are trained for a single use case. We propose to use the frequency
selectivity principle, where a frequency model is estimated locally that
approximates the surface of the point cloud. Then, additional points are
inserted into the approximated surface. Our novel frequency-selective geometry
upsampling shows superior results in terms of subjective as well as objective
quality compared to state-of-the-art methods for scaling factors of 2 and 4. On
average, our proposed method shows a 4.4 times smaller point-to-point error
than the second best state-of-the-art PU-Net for a scale factor of 4.
