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With the increasing demand of capturing our environment in three-dimensionsfor AR/ VR applications and autonomous driving among others, the importance ofhigh-resolution point clouds rises. As the capturing process is a complex task,point cloud upsampling is often desired. We propose Frequency-SelectiveUpsampling (FSU), an upsampling scheme that upsamples geometry and attributeinformation of point clouds jointly in a sequential manner with overlappedsupport areas. The point cloud is partitioned into blocks with overlappingsupport area first. Then, a continuous frequency model is generated thatestimates the point cloud's surface locally. The model is sampled at newpositions for upsampling. In a subsequent step, another frequency model iscreated that models the attribute signal. Here, knowledge from the geometryupsampling is exploited for a simplified projection of the points in twodimensions. The attribute model is evaluated for the upsampled geometrypositions. In our extensive evaluation, we evaluate geometry and attributeupsampling independently and show joint results. The geometry results show bestperformances for our proposed FSU in terms of point-to-plane error andplane-to-plane angular similarity. Moreover, FSU outperforms other colorupsampling schemes by 1.9 dB in terms of color PSNR. In addition, the visualappearance of the point clouds clearly increases with FSU.

Joint Geometry and Attribute Upsampling of Point Clouds Using Frequency-Selective Models with Overlapped Support