  Single-view 3D object reconstruction has seen much progress, yet methods
still struggle generalizing to novel shapes unseen during training. Common
approaches predominantly rely on learned global shape priors and, hence,
disregard detailed local observations. In this work, we address this issue by
learning a hierarchy of priors at different levels of locality from ground
truth input depth maps. We argue that exploiting local priors allows our method
to efficiently use input observations, thus improving generalization in visible
areas of novel shapes. At the same time, the combination of local and global
priors enables meaningful hallucination of unobserved parts resulting in
consistent 3D shapes. We show that the hierarchical approach generalizes much
better than the global approach. It generalizes not only between different
instances of a class but also across classes and to unseen arrangements of
objects.
