  In this paper, we deal with the problem of monocular depth estimation for
fisheye cameras in a self-supervised manner. A known issue of self-supervised
depth estimation is that it suffers in low-light/over-exposure conditions and
in large homogeneous regions. To tackle this issue, we propose a novel ordinal
distillation loss that distills the ordinal information from a large teacher
model. Such a teacher model, since having been trained on a large amount of
diverse data, can capture the depth ordering information well, but lacks in
preserving accurate scene geometry. Combined with self-supervised losses, we
show that our model can not only generate reasonable depth maps in challenging
environments but also better recover the scene geometry. We further leverage
the fisheye cameras of an AR-Glasses device to collect an indoor dataset to
facilitate evaluation.
