Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus, the methods need to train a specific model for each scale factor, which is both inefficient and impractical for storage and computation in real applications. To address this limitation, in this work, we propose a novel method called ``Meta-PU" to firstly support point cloud upsampling of arbitrary scale factors with a single model. In the Meta-PU method, besides the backbone network consisting of residual graph convolution (RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC blocks dynamically, and a farthest sampling block is adopted to sample different numbers of points. Together, these two blocks enable our Meta-PU to continuously upsample the point cloud with arbitrary scale factors by using only a single model. In addition, the experiments reveal that training on multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.

中文翻译：

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Meta-PU：点云的任意规模上采样网络

点云上采样对于三维重建中的网格质量至关重要。由于深度学习的发展，最近关于点云上采样的研究取得了巨大的成功。然而，现有方法将不同比例因子的点云上采样视为独立任务。因此，这些方法需要针对每个比例因子训练一个特定的模型，这对于实际应用中的存储和计算而言既低效又不切实际。为了解决这一局限性，在这项工作中，我们提出了一种称为“ Meta-PU”的新方法，该方法首先支持使用单个模型对任意比例因子进行点云上采样。图卷积（RGC）块，通过学习元子网动态调整RGC块的权重，采用最远的采样块对不同数量的点进行采样。这两个模块一起使我们的Meta-PU仅使用一个模型就可以使用任意比例因子连续对点云进行升采样。此外，实验表明，同时进行多个规模的训练对彼此有益。因此，Meta-PU甚至优于仅针对特定比例因子训练的现有方法。