  Scanning real-life scenes with modern registration devices typically give
incomplete point cloud representations, mostly due to the limitations of the
scanning process and 3D occlusions. Therefore, completing such partial
representations remains a fundamental challenge of many computer vision
applications. Most of the existing approaches aim to solve this problem by
learning to reconstruct individual 3D objects in a synthetic setup of an
uncluttered environment, which is far from a real-life scenario. In this work,
we reformulate the problem of point cloud completion into an object
hallucination task. Thus, we introduce a novel autoencoder-based architecture
called HyperPocket that disentangles latent representations and, as a result,
enables the generation of multiple variants of the completed 3D point clouds.
We split point cloud processing into two disjoint data streams and leverage a
hypernetwork paradigm to fill the spaces, dubbed pockets, that are left by the
missing object parts. As a result, the generated point clouds are not only
smooth but also plausible and geometrically consistent with the scene. Our
method offers competitive performances to the other state-of-the-art models,
and it enables a~plethora of novel applications.
