Rendering an accurate image of an isosurface in a volumetric field typically
requires large numbers of data samples. Reducing the number of required samples
lies at the core of research in volume rendering. With the advent of deep
learning networks, a number of architectures have been proposed recently to
infer missing samples in multi-dimensional fields, for applications such as
image super-resolution and scan completion. In this paper, we investigate the
use of such architectures for learning the upscaling of a low-resolution
sampling of an isosurface to a higher resolution, with high fidelity
reconstruction of spatial detail and shading. We introduce a fully
convolutional neural network, to learn a latent representation generating a
smooth, edge-aware normal field and ambient occlusions from a low-resolution
normal and depth field. By adding a frame-to-frame motion loss into the
learning stage, the upscaling can consider temporal variations and achieves
improved frame-to-frame coherence. We demonstrate the quality of the network
for isosurfaces which were never seen during training, and discuss remote and
in-situ visualization as well as focus+context visualization as potential
application