  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.
