  The development in the field of autonomous driving goes hand in hand with
ever new developments in the field of image processing and machine learning
methods. In order to fully exploit the advantages of deep learning, it is
necessary to have sufficient labeled training data available. This is
especially not the case for omnidirectional fisheye cameras. As a solution, we
propose in this paper to use synthetic training data based on Unity3D. A
five-pass algorithm is used to create a virtual fisheye camera. This synthetic
training data is evaluated for the application of free space detection for
different deep learning network architectures. The results indicate that
synthetic fisheye images can be used in deep learning context.
