  In this paper, we present a novel unpaired point cloud completion network,
named Cycle4Completion, to infer the complete geometries from a partial 3D
object. Previous unpaired completion methods merely focus on the learning of
geometric correspondence from incomplete shapes to complete shapes, and ignore
the learning in the reverse direction, which makes them suffer from low
completion accuracy due to the limited 3D shape understanding ability. To
address this problem, we propose two simultaneous cycle transformations between
the latent spaces of complete shapes and incomplete ones. The insight of cycle
transformation is to promote networks to understand 3D shapes by learning to
generate complete or incomplete shapes from their complementary ones.
Specifically, the first cycle transforms shapes from incomplete domain to
complete domain, and then projects them back to the incomplete domain. This
process learns the geometric characteristic of complete shapes, and maintains
the shape consistency between the complete prediction and the incomplete input.
Similarly, the inverse cycle transformation starts from complete domain to
incomplete domain, and goes back to complete domain to learn the characteristic
of incomplete shapes. We provide a comprehensive evaluation in experiments,
which shows that our model with the learned bidirectional geometry
correspondence outperforms state-of-the-art unpaired completion methods.
