  Advanced Driver-Assistance Systems rely heavily on perception tasks such as
semantic segmentation where images are captured from large field of view (FoV)
cameras. State-of-the-art works have made considerable progress toward applying
Convolutional Neural Network (CNN) to standard (rectilinear) images. However,
the large FoV cameras used in autonomous vehicles produce fisheye images
characterized by strong geometric distortion. This work demonstrates that a CNN
trained on standard images can be readily adapted to fisheye images, which is
crucial in real-world applications where time-consuming real-time data
transformation must be avoided. Our adaptation protocol mainly relies on
modifying the support of the convolutions by using their deformable equivalents
on top of pre-existing layers. We prove that tuning an optimal support only
requires a limited amount of labeled fisheye images, as a small number of
training samples is sufficient to significantly improve an existing model's
performance on wide-angle images. Furthermore, we show that finetuning the
weights of the network is not necessary to achieve high performance once the
deformable components are learned. Finally, we provide an in-depth analysis of
the effect of the deformable convolutions, bringing elements of discussion on
the behavior of CNN models.
