  In this paper, we tackle the challenging problem of point cloud completion
from the perspective of feature learning. Our key observation is that to
recover the underlying structures as well as surface details, given partial
input, a fundamental component is a good feature representation that can
capture both global structure and local geometric details. We accordingly first
propose FSNet, a feature structuring module that can adaptively aggregate
point-wise features into a 2D structured feature map by learning multiple
latent patterns from local regions. We then integrate FSNet into a
coarse-tofine pipeline for point cloud completion. Specifically, a 2D
convolutional neural network is adopted to decode feature maps from FSNet into
a coarse and complete point cloud. Next, a point cloud upsampling network is
used to generate a dense point cloud from the partial input and the coarse
intermediate output. To efficiently exploit local structures and enhance point
distribution uniformity, we propose IFNet, a point upsampling module with a
self-correction mechanism that can progressively refine details of the
generated dense point cloud. We have conducted qualitative and quantitative
experiments on ShapeNet, MVP, and KITTI datasets, which demonstrate that our
method outperforms state-of-theart point cloud completion approaches.
