  Shape completion aims to recover the full 3D geometry of an object from a
partial observation. This problem is inherently multi-modal since there can be
many ways to plausibly complete the missing regions of a shape. Such diversity
would be indicative of the underlying uncertainty of the shape and could be
preferable for downstream tasks such as planning. In this paper, we propose a
novel conditional generative adversarial network that can produce many diverse
plausible completions of a partially observed point cloud. To enable our
network to produce multiple completions for the same partial input, we
introduce stochasticity into our network via style modulation. By extracting
style codes from complete shapes during training, and learning a distribution
over them, our style codes can explicitly carry shape category information
leading to better completions. We further introduce diversity penalties and
discriminators at multiple scales to prevent conditional mode collapse and to
train without the need for multiple ground truth completions for each partial
input. Evaluations across several synthetic and real datasets demonstrate that
our method achieves significant improvements in respecting the partial
observations while obtaining greater diversity in completions.
