Conventional convolution neural networks (CNNs) trained on narrow Field-of-View (FoV) images are the state-of-the art approaches for object recognition tasks. Some methods proposed adaptation of CNNs to ultra-wide FoV by learning deformable kernels. However, they limited Euclidean geometry and their accuracy degrades under strong distortions caused fisheye projections. In this work, we demonstr...