  We consider the problem of scaling deep generative shape models to
high-resolution. Drawing motivation from the canonical view representation of
objects, we introduce a novel method for the fast up-sampling of 3D objects in
voxel space through networks that perform super-resolution on the six
orthographic depth projections. This allows us to generate high-resolution
objects with more efficient scaling than methods which work directly in 3D. We
decompose the problem of 2D depth super-resolution into silhouette and depth
prediction to capture both structure and fine detail. This allows our method to
generate sharp edges more easily than an individual network. We evaluate our
work on multiple experiments concerning high-resolution 3D objects, and show
our system is capable of accurately predicting novel objects at resolutions as
large as 512$\mathbf{\times}$512$\mathbf{\times}$512 -- the highest resolution
reported for this task. We achieve state-of-the-art performance on 3D object
reconstruction from RGB images on the ShapeNet dataset, and further demonstrate
the first effective 3D super-resolution method.
