  Point completion refers to complete the missing geometries of objects from
partial point clouds. Existing works usually estimate the missing shape by
decoding a latent feature encoded from the input points. However, real-world
objects are usually with diverse topologies and surface details, which a latent
feature may fail to represent to recover a clean and complete surface. To this
end, we propose a skeleton-bridged point completion network (SK-PCN) for shape
completion. Given a partial scan, our method first predicts its 3D skeleton to
obtain the global structure, and completes the surface by learning
displacements from skeletal points. We decouple the shape completion into
structure estimation and surface reconstruction, which eases the learning
difficulty and benefits our method to obtain on-surface details. Besides,
considering the missing features during encoding input points, SK-PCN adopts a
local adjustment strategy that merges the input point cloud to our predictions
for surface refinement. Comparing with previous methods, our skeleton-bridged
manner better supports point normal estimation to obtain the full surface mesh
beyond point clouds. The qualitative and quantitative experiments on both point
cloud and mesh completion show that our approach outperforms the existing
methods on various object categories.
