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.

介绍了一种快速升采样 3D 物体到高分辨率的方法，通过在六个正交深度投影上进行超分辨率网络，可以比直接在 3D 空间中工作的方法更高效地生成高分辨率物体，该方法已被证明在 ShapeNet 数据集上表现良好，并在 3D 重建方面实现了最先进的性能。