  In this work, we exploit a depth estimation Fully Convolutional Residual
Neural Network (FCRN) for in-air perspective images to estimate the depth of
underwater perspective and omni-directional images. We train one conventional
and one spherical FCRN for underwater perspective and omni-directional images,
respectively. The spherical FCRN is derived from the perspective FCRN via a
spherical longitude-latitude mapping. For that, the omni-directional camera is
modeled as a sphere, while images captured by it are displayed in the
longitude-latitude form. Due to the lack of underwater datasets, we synthesize
images in both data-driven and theoretical ways, which are used in training and
testing. Finally, experiments are conducted on these synthetic images and
results are displayed in both qualitative and quantitative way. The comparison
between ground truth and the estimated depth map indicates the effectiveness of
our method.
