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

A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling