  With the increased use of virtual and augmented reality applications, the
importance of point cloud data rises. High-quality capturing of point clouds is
still expensive and thus, the need for point cloud super-resolution or point
cloud upsampling techniques emerges. In this paper, we propose an interpolation
scheme for color upsampling of three-dimensional color point clouds. As a point
cloud represents an object's surface in three-dimensional space, we first
conduct a local transform of the surface into a two-dimensional plane.
Secondly, we propose to apply a novel Frequency-Selective Mesh-to-Mesh
Resampling (FSMMR) technique for the interpolation of the points in 2D. FSMMR
generates a model of weighted superpositions of basis functions on scattered
points. This model is then evaluated for the final points in order to increase
the resolution of the original point cloud. Evaluation shows that our approach
outperforms common interpolation schemes. Visual comparisons of the jaguar
point cloud underlines the quality of our upsampling results. The high
performance of FSMMR holds for various sampling densities of the input point
cloud.
