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As many different 3D volumes could produce the same 2D x-ray image, invertingthis process is challenging. We show that recent deep learning-basedconvolutional neural networks can solve this task. As the main challenge inlearning is the sheer amount of data created when extending the 2D image into a3D volume, we suggest firstly to learn a coarse, fixed-resolution volume whichis then fused in a second step with the input x-ray into a high-resolutionvolume. To train and validate our approach we introduce a new dataset thatcomprises of close to half a million computer-simulated 2D x-ray images of 3Dvolumes scanned from 175 mammalian species. Applications of our approachinclude stereoscopic rendering of legacy x-ray images, re-rendering of x-raysincluding changes of illumination, view pose or geometry. Our evaluationincludes comparison to previous tomography work, previous learning methodsusing our data, a user study and application to a set of real x-rays.

Single-image Tomography: 3D Volumes from 2D Cranial X-Rays