  Volume Rendering is an important technique for visualizing three-dimensional
scalar data grids and is commonly employed for scientific and medical image
data. Direct Volume Rendering (DVR) is a well established and efficient
rendering algorithm for volumetric data. Neural rendering uses deep neural
networks to solve inverse rendering tasks and applies techniques similar to
DVR. However, it has not been demonstrated successfully for the rendering of
scientific volume data.
  In this work, we introduce Deep Direct Volume Rendering (DeepDVR), a
generalization of DVR that allows for the integration of deep neural networks
into the DVR algorithm. We conceptualize the rendering in a latent color space,
thus enabling the use of deep architectures to learn implicit mappings for
feature extraction and classification, replacing explicit feature design and
hand-crafted transfer functions. Our generalization serves to derive novel
volume rendering architectures that can be trained end-to-end directly from
examples in image space, obviating the need to manually define and fine-tune
multidimensional transfer functions while providing superior classification
strength. We further introduce a novel stepsize annealing scheme to accelerate
the training of DeepDVR models and validate its effectiveness in a set of
experiments. We validate our architectures on two example use cases: (1)
learning an optimized rendering from manually adjusted reference images for a
single volume and (2) learning advanced visualization concepts like shading and
semantic colorization that generalize to unseen volume data.
  We find that deep volume rendering architectures with explicit modeling of
the DVR pipeline effectively enable end-to-end learning of scientific volume
rendering tasks from target images.
