  In this paper we explore the recent topic of point cloud completion, guided
by an auxiliary image. We show how it is possible to effectively combine the
information from the two modalities in a localized latent space, thus avoiding
the need for complex point cloud reconstruction methods from single views used
by the state-of-the-art. We also investigate a novel weakly-supervised setting
where the auxiliary image provides a supervisory signal to the training process
by using a differentiable renderer on the completed point cloud to measure
fidelity in the image space. Experiments show significant improvements over
state-of-the-art supervised methods for both unimodal and multimodal
completion. We also show the effectiveness of the weakly-supervised approach
which outperforms a number of supervised methods and is competitive with the
latest supervised models only exploiting point cloud information.
