  In recent years, point cloud upsampling has been widely applied in fields
such as 3D reconstruction and surface generation. However, existing point cloud
upsampling inputs are all patch based, and there is no research discussing the
differences and principles between point cloud model full input and patch based
input. In order to compare with patch based point cloud input, this article
proposes a new data input method, which divides the full point cloud model to
ensure shape integrity while training PU-GCN. This article was validated on the
PU1K and ABC datasets, but the results showed that Patch based performance is
better than model based full input i.e. Average Segment input. Therefore, this
article explores the data input factors and model modules that affect the
upsampling results of point clouds.
