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.

本文提出了一种基于变分关系点补全网络（VRCNet）的框架，采用概率建模和关系增强技术，实现对不完整点云进行准确重建，并达到对部分点云进行 3D 分类和模型识别的目的。

变分关系点补全网络用于稳定的 3D 分类

Variational Relational Point Completion Network for Robust 3D  Classification

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.

本文提出了一种基于变分框架的点云重建方法 (Variational Relational point Completion network - VRCNet)，该方法采用了概率建模和关系增强技术实现点云的精细局部重建，同时给出了一个包含 10 万多个高质量扫描的多视角局部点云数据集 (MVP 数据集)，实验证明 VRCNet 在标准点云重建基准上的性能优于目前的最先进方法，并且在真实世界点云扫描中表现出了很好的泛化性和鲁棒性。