  Several deep learning methods have been proposed for completing partial data
from shape acquisition setups, i.e., filling the regions that were missing in
the shape. These methods, however, only complete the partial shape with a
single output, ignoring the ambiguity when reasoning the missing geometry.
Hence, we pose a multi-modal shape completion problem, in which we seek to
complete the partial shape with multiple outputs by learning a one-to-many
mapping. We develop the first multimodal shape completion method that completes
the partial shape via conditional generative modeling, without requiring paired
training data. Our approach distills the ambiguity by conditioning the
completion on a learned multimodal distribution of possible results. We
extensively evaluate the approach on several datasets that contain varying
forms of shape incompleteness, and compare among several baseline methods and
variants of our methods qualitatively and quantitatively, demonstrating the
merit of our method in completing partial shapes with both diversity and
quality.
