PU-Net: Point Cloud Upsampling Network
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Learning and analyzing 3D point cloud with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud upsampling technique. The key idea is to learn multi-level features per point, and then expanding them via a multi-branch convolution unit, to implicitly expand the point set in feature space. The expanded feature is then split to a multitude of features, which are then reconstructed to an upsampled point set. Our network is applied at a patch-level, with a joint loss function that encourages the upsampled points to remain on the underlying surface with a uniform distribution. We conduct various experiments using synthesis and scan data to evaluate our method and demonstrate its superiority over some baseline methods and an optimization-based method. The results show that our upsampled results have better uniformity, and sampled closer to the underlying surface.
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