Unsupervised point cloud completion aims at estimating the corresponding
complete point cloud of a partial point cloud in an unpaired manner. It is a
crucial but challenging problem since there is no paired partial-complete
supervision that can be exploited directly. In this work, we propose a novel
framework, which learns a unified and structured latent space that encoding
both partial and complete point clouds. Specifically, we map a series of
related partial point clouds into multiple complete shape and occlusion code
pairs and fuse the codes to obtain their representations in the unified latent
space. To enforce the learning of such a structured latent space, the proposed
method adopts a series of constraints including structured ranking
regularization, latent code swapping constraint, and distribution supervision
on the related partial point clouds. By establishing such a unified and
structured latent space, better partial-complete geometry consistency and shape
completion accuracy can be achieved. Extensive experiments show that our
proposed method consistently outperforms state-of-the-art unsupervised methods
on both synthetic ShapeNet and real-world KITTI, ScanNet, and Matterport3D
datasets.

本文提出了一种新的框架，学习了一个统一的、结构化的潜在空间，其中包括映射相关局部点云的多个完整的形状和遮挡代码对，并将这些代码合并以获取它们在统一潜在空间中的表示，通过建立这样一个统一的和结构化潜在空间，可以实现更好的局部 - 完整几何一致性和形状完成准确性，并在合成和现实世界的各种数据集上比现有的无监督方法表现更为出色。