Self-Supervised Monocular Depth Underwater

Shlomi Amitai, Itzik Klein, Tali Treibitz

Abstract: Depth estimation is critical for any robotic system. In the past years, the estimation of depth from monocular images has 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 in overcoming this. Moreover, underwater, there are more limitations to using high-resolution depth sensors, which is a serious obstacle to generating ground truth. So far unsupervised methods that tried to solve this have achieved limited success as they relied on domain transfer from a dataset in the air. We suggest network training using subsequent frames, self-supervised by a reprojection loss, as was demonstrated successfully above water. We propose 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.