  Depth estimation is critical for any robotic system. In the past years
estimation of depth from monocular images have shown great improvement,
however, in the underwater environment results are still lagging behind due to
appearance changes caused by the medium. So far little effort has been invested
on overcoming this. Moreover, underwater, there are more limitations for using
high resolution depth sensors, this makes generating ground truth for learning
methods another enormous obstacle. So far unsupervised methods that tried to
solve this have achieved very limited success as they relied on domain transfer
from dataset in air. We suggest training using subsequent frames
self-supervised by a reprojection loss, as was demonstrated successfully above
water. We suggest several additions to the self-supervised framework to cope
with the underwater environment and achieve state-of-the-art results on a
challenging forward-looking underwater dataset.
