  Real-scanned point clouds are often incomplete due to viewpoint, occlusion,
and noise, which hampers 3D geometric modeling and perception. 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 multi-view partial point cloud datasets (MVP and MVP-40
dataset) containing over 200,000 high-quality scans, which render partial 3D
shapes from 26 uniformly distributed camera poses for each 3D CAD model.
Extensive experiments demonstrate that VRCNet outperforms state-of-the-art
methods on all standard point cloud completion benchmarks. Notably, VRCNet
shows great generalizability and robustness on real-world point cloud scans.
Moreover, we can achieve robust 3D classification for partial point clouds with
the help of VRCNet, which can highly increase classification accuracy.
