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.

本文提出了一种数据驱动的三维点云上采样技术，通过在特征空间中隐式地学习多层次特征并扩展点集，进而构建高密度点集，并在表面均匀分布的基础上实现采样点的重构，实验结果表明该方法的优越性。