Ye Shuquan, Chen Dongdong, Han Songfang, Wan Ziyu, Liao Jing
IEEE Trans Vis Comput Graph. 2022 Sep;28(9):3206-3218. doi: 10.1109/TVCG.2021.3058311. Epub 2022 Jul 29.
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 article, we propose a novel method called "Meta-PU" to first 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.
点云上采样对于三维重建中网格的质量至关重要。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成功。然而，现有方法将不同缩放因子的点云上采样视为独立任务。因此，这些方法需要为每个缩放因子训练一个特定模型，这在实际应用中对于存储和计算而言既低效又不实用。为了解决这一局限性，在本文中，我们提出了一种名为“Meta-PU”的新方法，首先用单个模型支持任意缩放因子的点云上采样。在Meta-PU方法中，除了由残差图卷积（RGC）块组成的主干网络外，还学习了一个元子网来动态调整RGC块的权重，并采用最远采样块来采样不同数量的点。这两个块共同使我们的Meta-PU能够仅使用单个模型以任意缩放因子连续上采样点云。此外，实验表明在多个尺度上同时训练是相互有益的。因此，Meta-PU甚至优于仅针对特定缩放因子训练的现有方法。
