  We present 3DeepCT, a deep neural network for computed tomography, which
performs 3D reconstruction of scattering volumes from multi-view images. Our
architecture is dictated by the stationary nature of atmospheric cloud fields.
The task of volumetric scattering tomography aims at recovering a volume from
its 2D projections. This problem has been studied extensively, leading, to
diverse inverse methods based on signal processing and physics models. However,
such techniques are typically iterative, exhibiting high computational load and
long convergence time. We show that 3DeepCT outperforms physics-based inverse
scattering methods in term of accuracy as well as offering a significant orders
of magnitude improvement in computational time. To further improve the recovery
accuracy, we introduce a hybrid model that combines 3DeepCT and physics-based
method. The resultant hybrid technique enjoys fast inference time and improved
recovery performance.
