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

Philipp Henzler (Ulm University)   Volker Rasche (Ulm University)
Timo Ropinski (Ulm University)   Tobias Ritschel (UCL London)

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

Philipp Henzler, Volker Rasche, Timo Ropinski, Tobias Ritschel:
Single-image Tomography: 3D Volumes from 2D X-Rays

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  inproceedings = {  arXiv:1710.04867 [cs.GR]},
  title = {{How do Recent Machine Learning Advances Impact the Data Visualization Research Agenda?}},
  author = {Henzler, Philipp and Rasche, Volker and Ropinski, Timo and Ritschel, Tobias},
  year = {2017}