PU Net: Point Cloud Upsampling Network
Learning and analyzing 3D point clouds 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 expand the point set via a multi branch convolution unit implicitly 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. Results show that our upsampled points have better uniformity and are located closer to the underlying surfaces.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition