  Point cloud completion aims at completing geometric and topological shapes
from a partial observation. However, some topology of the original shape is
missing, existing methods directly predict the location of complete points,
without predicting structured and topological information of the complete
shape, which leads to inferior performance. To better tackle the missing
topology part, we propose LAKe-Net, a novel topology-aware point cloud
completion model by localizing aligned keypoints, with a novel
Keypoints-Skeleton-Shape prediction manner. Specifically, our method completes
missing topology using three steps: 1) Aligned Keypoint Localization. An
asymmetric keypoint locator, including an unsupervised multi-scale keypoint
detector and a complete keypoint generator, is proposed for localizing aligned
keypoints from complete and partial point clouds. We theoretically prove that
the detector can capture aligned keypoints for objects within a sub-category.
2) Surface-skeleton Generation. A new type of skeleton, named Surface-skeleton,
is generated from keypoints based on geometric priors to fully represent the
topological information captured from keypoints and better recover the local
details. 3) Shape Refinement. We design a refinement subnet where multi-scale
surface-skeletons are fed into each recursive skeleton-assisted refinement
module to assist the completion process. Experimental results show that our
method achieves the state-of-the-art performance on point cloud completion.
