  Optical flow estimation with convolutional neural networks (CNNs) has
recently solved various tasks of computer vision successfully. In this paper we
adapt a state-of-the-art approach for optical flow estimation to
omnidirectional images. We investigate CNN architectures to determine high
motion variations caused by the geometry of fish-eye images. Further we
determine the qualitative influence of texture on the non-rigid object to the
motion vectors. For evaluation of the results we create ground truth motion
fields synthetically. The ground truth contains cubes with static background.
We test variations of pre-trained FlowNet 2.0 architectures by indicating
common error metrics. We generate competitive results for the motion of the
foreground with inhomogeneous texture on the moving object.
