  Real-scanned point clouds are often incomplete due to viewpoint, occlusion,
and noise. Existing point cloud completion methods tend to generate global
shape skeletons and hence lack fine local details. Furthermore, they mostly
learn a deterministic partial-to-complete mapping, but overlook structural
relations in man-made objects. To tackle these challenges, this paper proposes
a variational framework, Variational Relational point Completion network
(VRCNet) with two appealing properties: 1) Probabilistic Modeling. In
particular, we propose a dual-path architecture to enable principled
probabilistic modeling across partial and complete clouds. One path consumes
complete point clouds for reconstruction by learning a point VAE. The other
path generates complete shapes for partial point clouds, whose embedded
distribution is guided by distribution obtained from the reconstruction path
during training. 2) Relational Enhancement. Specifically, we carefully design
point self-attention kernel and point selective kernel module to exploit
relational point features, which refines local shape details conditioned on the
coarse completion. In addition, we contribute a multi-view partial point cloud
dataset (MVP dataset) containing over 100,000 high-quality scans, which renders
partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD
model. Extensive experiments demonstrate that VRCNet outperforms
state-of-theart methods on all standard point cloud completion benchmarks.
Notably, VRCNet shows great generalizability and robustness on real-world point
cloud scans.
