Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction. However,they usually suffer from two critical issues: (1)fixed upsampling rate after one-time training, since the feature expansion unit is customized for each upsampling rate; (2)outliers or shrinkage artifact caused by the difficulty of precisely predicting 3D coordinates or residuals of upsampled points. To adress them, we propose a new framework for accurate point cloud upsampling that supports arbitrary upsampling rates. Our method first interpolates the low-res point cloud according to a given upsampling rate. And then refine the positions of the interpolated points with an iterative optimization process, guided by a trained model estimating the difference between the current point cloud and the high-res target. Extensive quantitative and qualitative results on benchmarks and downstream tasks demonstrate that our method achieves the state-of-the-art accuracy and efficiency.

翻译：现有的大多数点云上采样方法大致分为三步：特征提取、特征扩展和三维坐标预测。然而，它们通常面临两个关键问题：(1) 由于特征扩展单元是为特定上采样率定制的，因此在单次训练后无法支持可变上采样率；(2) 由于难以精确预测上采样点的三维坐标或残差，导致出现离群点或收缩伪影。为解决这些问题，我们提出了一种能够支持任意上采样率的高精度点云上采样新框架。该方法首先根据给定的上采样率对低分辨率点云进行插值，然后通过迭代优化过程精化插值点的位置，该过程由训练好的模型引导，用于评估当前点云与高分辨率目标之间的差异。在基准数据集和下游任务上的大量定量和定性结果表明，我们的方法在精度和效率上均达到了当前最优水平。