  Optical flow estimation is a well-studied topic for automated driving
applications. Many outstanding optical flow estimation methods have been
proposed, but they become erroneous when tested in challenging scenarios that
are commonly encountered. Despite the increasing use of fisheye cameras for
near-field sensing in automated driving, there is very limited literature on
optical flow estimation with strong lens distortion. Thus we propose and
evaluate training strategies to improve a learning-based optical flow algorithm
by leveraging the only existing fisheye dataset with optical flow ground truth.
While trained with synthetic data, the model demonstrates strong capabilities
to generalize to real world fisheye data. The other challenge neglected by
existing state-of-the-art algorithms is low light. We propose a novel, generic
semi-supervised framework that significantly boosts performances of existing
methods in such conditions. To the best of our knowledge, this is the first
approach that explicitly handles optical flow estimation in low light.

Computer Vision and Pattern Recognition;Robotics