  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.
