learning a structured latent space for unsupervised point cloud completion

Yingjie CAI, Kwan-Yee Lin, Chao Zhang, Qiang Wang, Xiaogang Wang, Hongsheng Li

Abstract:

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.