  We present a novel deep learning based technique for volumetric ambient
occlusion in the context of direct volume rendering. Our proposed Deep
Volumetric Ambient Occlusion (DVAO) approach can predict per-voxel ambient
occlusion in volumetric data sets, while considering global information
provided through the transfer function. The proposed neural network only needs
to be executed upon change of this global information, and thus supports
real-time volume interaction. Accordingly, we demonstrate DVAOs ability to
predict volumetric ambient occlusion, such that it can be applied interactively
within direct volume rendering. To achieve the best possible results, we
propose and analyze a variety of transfer function representations and
injection strategies for deep neural networks. Based on the obtained results we
also give recommendations applicable in similar volume learning scenarios.
Lastly, we show that DVAO generalizes to a variety of modalities, despite being
trained on computed tomography data only.
