  Fisheye cameras are commonly used in applications like autonomous driving and
surveillance to provide a large field of view ($>180^{\circ}$). However, they
come at the cost of strong non-linear distortions which require more complex
algorithms. In this paper, we explore Euclidean distance estimation on fisheye
cameras for automotive scenes. Obtaining accurate and dense depth supervision
is difficult in practice, but self-supervised learning approaches show
promising results and could potentially overcome the problem. We present a
novel self-supervised scale-aware framework for learning Euclidean distance and
ego-motion from raw monocular fisheye videos without applying rectification.
While it is possible to perform piece-wise linear approximation of fisheye
projection surface and apply standard rectilinear models, it has its own set of
issues like re-sampling distortion and discontinuities in transition regions.
To encourage further research in this area, we will release our dataset as part
of the WoodScape project \cite{yogamani2019woodscape}. We further evaluated the
proposed algorithm on the KITTI dataset and obtained state-of-the-art results
comparable to other self-supervised monocular methods. Qualitative results on
an unseen fisheye video demonstrate impressive performance
https://youtu.be/Sgq1WzoOmXg.
