# Unsupervised Single Image Underwater Depth Estimation

## TL;DR

This paper introduces an unsupervised approach for estimating depth from a single underwater image using haze cues and cycle-consistent learning, overcoming the lack of large datasets and ground truth depth maps.

## Contribution

It presents a novel unsupervised method leveraging unpaired terrestrial and underwater images with dense-block auto-encoders, achieving state-of-the-art results.

## Key findings

- State-of-the-art accuracy in underwater depth estimation
- Effective use of haze as a depth cue
- Robust performance across diverse underwater conditions

## Abstract

Depth estimation from a single underwater image is one of the most challenging problems and is highly ill-posed. Due to the absence of large generalized underwater depth datasets and the difficulty in obtaining ground truth depth-maps, supervised learning techniques such as direct depth regression cannot be used. In this paper, we propose an unsupervised method for depth estimation from a single underwater image taken `in the wild' by using haze as a cue for depth. Our approach is based on indirect depth-map estimation where we learn the mapping functions between unpaired RGB-D terrestrial images and arbitrary underwater images to estimate the required depth-map. We propose a method which is based on the principles of cycle-consistent learning and uses dense-block based auto-encoders as generator networks. We evaluate and compare our method both quantitatively and qualitatively on various underwater images with diverse attenuation and scattering conditions and show that our method produces state-of-the-art results for unsupervised depth estimation from a single underwater image.