  Deep neural networks have been successfully applied in many different fields
like computational imaging, medical healthcare, signal processing, or
autonomous driving. In a proof-of-principle study, we demonstrate that
computational optical form measurement can also benefit from deep learning. A
data-driven machine learning approach is explored to solve an inverse problem
in the accurate measurement of optical surfaces. The approach is developed and
tested using virtual measurements with known ground truth.
