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.

翻译：随着AR/VR应用及自动驾驶等领域对三维环境捕捉需求的日益增长，高分辨率点云的重要性不断提升。由于捕获过程是一项复杂任务，点云上采样通常成为必要环节。我们提出频率选择性上采样（FSU），这是一种通过重叠支撑区域顺序联合上采样点云几何与属性信息的方案。首先将点云划分为具有重叠支撑区域的块，随后生成连续频率模型以局部估计点云表面，并在新位置对该模型进行采样实现上采样。下一步，建立另一个频率模型来建模属性信号，此处利用几何上采样的知识简化点云的二维投影过程，并对上采样后的几何位置评估属性模型。在广泛评估中，我们分别独立测试几何与属性上采样性能并展示联合结果。几何结果表明，我们提出的FSU在点到平面误差和平面到平面角度相似性方面表现最佳。此外，FSU在颜色PSNR上比其它颜色上采样方案高出1.9 dB，同时FSU显著提升了点云的视觉外观。