  Point clouds produced by 3D scanning are often sparse, non-uniform, and
noisy. Recent upsampling approaches aim to generate a dense point set, while
achieving both distribution uniformity and proximity-to-surface, and possibly
amending small holes, all in a single network. After revisiting the task, we
propose to disentangle the task based on its multi-objective nature and
formulate two cascaded sub-networks, a dense generator and a spatial refiner.
The dense generator infers a coarse but dense output that roughly describes the
underlying surface, while the spatial refiner further fine-tunes the coarse
output by adjusting the location of each point. Specifically, we design a pair
of local and global refinement units in the spatial refiner to evolve a coarse
feature map. Also, in the spatial refiner, we regress a per-point offset vector
to further adjust the coarse outputs in fine-scale. Extensive qualitative and
quantitative results on both synthetic and real-scanned datasets demonstrate
the superiority of our method over the state-of-the-arts.
