  Monocular depth estimation has experienced significant progress on
terrestrial images in recent years, largely due to deep learning advancements.
However, it remains inadequate for underwater scenes, primarily because of data
scarcity. Given the inherent challenges of light attenuation and backscattering
in water, acquiring clear underwater images or precise depth information is
notably difficult and costly. Consequently, learning-based approaches often
rely on synthetic data or turn to unsupervised or self-supervised methods to
mitigate this lack of data. Nonetheless, the performance of these methods is
often constrained by the domain gap and looser constraints. In this paper, we
propose a novel pipeline for generating photorealistic underwater images using
accurate terrestrial depth data. This approach facilitates the training of
supervised models for underwater depth estimation, effectively reducing the
performance disparity between terrestrial and underwater environments. Contrary
to prior synthetic datasets that merely apply style transfer to terrestrial
images without altering the scene content, our approach uniquely creates
vibrant, non-existent underwater scenes by leveraging terrestrial depth data
through the innovative Stable Diffusion model. Specifically, we introduce a
unique Depth2Underwater ControlNet, trained on specially prepared \{Underwater,
Depth, Text\} data triplets, for this generation task. Our newly developed
dataset enables terrestrial depth estimation models to achieve considerable
improvements, both quantitatively and qualitatively, on unseen underwater
images, surpassing their terrestrial pre-trained counterparts. Moreover, the
enhanced depth accuracy for underwater scenes also aids underwater image
restoration techniques that rely on depth maps, further demonstrating our
dataset's utility. The dataset will be available at
https://github.com/zkawfanx/Atlantis.

Computer Vision and Pattern Recognition