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.

通过预测三维骨架以及从骨架点学习偏移量，提出了一种骨架桥接点完形网络（SK-PCN），将形状完形分解为结构估计和表面重建，并采用本地调整策略合并输入点云以进行表面细化。实验表明，相比于现有方法，该方法在点云和网格完成的各个对象类别上表现更好。