  Most 3D shape completion approaches rely heavily on partial-complete shape
pairs and learn in a fully supervised manner. Despite their impressive
performances on in-domain data, when generalizing to partial shapes in other
forms or real-world partial scans, they often obtain unsatisfactory results due
to domain gaps. In contrast to previous fully supervised approaches, in this
paper we present ShapeInversion, which introduces Generative Adversarial
Network (GAN) inversion to shape completion for the first time. ShapeInversion
uses a GAN pre-trained on complete shapes by searching for a latent code that
gives a complete shape that best reconstructs the given partial input. In this
way, ShapeInversion no longer needs paired training data, and is capable of
incorporating the rich prior captured in a well-trained generative model. On
the ShapeNet benchmark, the proposed ShapeInversion outperforms the SOTA
unsupervised method, and is comparable with supervised methods that are learned
using paired data. It also demonstrates remarkable generalization ability,
giving robust results for real-world scans and partial inputs of various forms
and incompleteness levels. Importantly, ShapeInversion naturally enables a
series of additional abilities thanks to the involvement of a pre-trained GAN,
such as producing multiple valid complete shapes for an ambiguous partial
input, as well as shape manipulation and interpolation.
