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.

翻译：真实扫描的点云常因视角、遮挡和噪声而残缺不全，这阻碍了三维几何建模与感知。现有点云补全方法倾向于生成全局形状骨架，因此缺乏精细的局部细节。此外，它们多数学习确定性的部分到完整映射，却忽略了人造物体中的结构关系。为应对这些挑战，本文提出变分关系点云补全网络（VRCNet）这一变分框架，其具有两个吸引人的特性：1) 概率建模。具体而言，我们提出双路径架构以实现部分点云与完整点云间的原则性概率建模。一条路径通过学习点变分自编码器（VAE）消耗完整点云进行重建；另一条路径为部分点云生成完整形状，其嵌入分布在训练过程中受重建路径所得分布的引导。2) 关系增强。我们精心设计了点自注意力核与点选择核模块，以利用关系点特征，从而基于粗略补全结果细化局部形状细节。此外，我们贡献了多视角部分点云数据集（MVP和MVP-40数据集），包含超过20万个高质量扫描，每个三维CAD模型从26个均匀分布的相机姿态渲染出部分三维形状。大量实验表明，VRCNet在所有标准点云补全基准测试中均优于现有最优方法。值得注意的是，VRCNet在真实点云扫描中展现出强大的泛化能力和鲁棒性。借助VRCNet，我们还能实现对部分点云的鲁棒三维分类，显著提升分类精度。