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:随着对AR/ VR 应用程序和自主驱动等三个维度捕获我们环境的需求日益增加,高分辨率点云的重要性上升。由于抓取过程是一个复杂的任务,往往需要点云升取样。我们提议频率选择抽采(FSU),这是一个抽采办法,以相继方式将点云的几何和属性信息与重叠支持区域相衔接。点云被分割成块块,首先支持区域重叠。然后,产生连续频率模型,估计点云的表面。模型在新的取样位置取样。随后一步,另一个频率模型将建立属性信号的模型。这里,从几何抽采样中获取的知识被用于对点进行两个维度的简化预测。对点云的几何测量和属性模型进行了评估,以相重叠的支持区域为主。在我们的广泛评估中,我们评估了几何和属性,独立地标查取并展示了联合结果。几何结果显示我们提议的点云表面云表面表面的模型最佳性表现,以直观图像格式的外观性平面图案外的彩色图,通过平面图外的更色图外的更色图外的更色图外的图外的更色图外更。