  Recently, deep learning methods have shown great success in 3D point cloud
upsampling. Among these methods, many feature expansion units were proposed to
complete point expansion at the end. In this paper, we compare various feature
expansion units by both theoretical analysis and quantitative experiments. We
show that most of the existing feature expansion units process each point
feature independently, while ignoring the feature interaction among different
points. Further, inspired by upsampling module of image super-resolution and
recent success of dynamic graph CNN on point clouds, we propose a novel feature
expansion units named ProEdgeShuffle. Experiments show that our proposed method
can achieve considerable improvement over previous feature expansion units.
