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.

该研究针对 3D 扫描时获得的稀疏、非均匀和嘈杂点云，提出了一种基于多目标的两级级联网络，包括稠密生成器和空间细化器，能够生成密集的均匀点集，同时修补小洞并调整每个点的位置，结果表明该方法优于现有技术。