With the increasing demand of capturing our environment in three-dimensions
for AR/ VR applications and autonomous driving among others, the importance of
high-resolution point clouds rises. As the capturing process is a complex task,
point cloud upsampling is often desired. We propose Frequency-Selective
Upsampling (FSU), an upsampling scheme that upsamples geometry and attribute
information of point clouds jointly in a sequential manner with overlapped
support areas. The point cloud is partitioned into blocks with overlapping
support area first. Then, a continuous frequency model is generated that
estimates the point cloud's surface locally. The model is sampled at new
positions for upsampling. In a subsequent step, another frequency model is
created that models the attribute signal. Here, knowledge from the geometry
upsampling is exploited for a simplified projection of the points in two
dimensions. The attribute model is evaluated for the upsampled geometry
positions. In our extensive evaluation, we evaluate geometry and attribute
upsampling independently and show joint results. The geometry results show best
performances for our proposed FSU in terms of point-to-plane error and
plane-to-plane angular similarity. Moreover, FSU outperforms other color
upsampling schemes by 1.9 dB in terms of color PSNR. In addition, the visual
appearance of the point clouds clearly increases with FSU.

提出一种联合几何和属性信息进行点云上采样的频率选择性上采样（FSU）方法，在几何模型采样和属性模型评估两个步骤中利用块式覆盖支持来提高点云的表面详细度和视觉效果，并在实验证明，在误差和颜色 PSNR 等方面优于其他常规方案。