  Underwater imaging is a critical task performed by marine robots for a wide
range of applications including aquaculture, marine infrastructure inspection,
and environmental monitoring. However, water column effects, such as
attenuation and backscattering, drastically change the color and quality of
imagery captured underwater. Due to varying water conditions and
range-dependency of these effects, restoring underwater imagery is a
challenging problem. This impacts downstream perception tasks including depth
estimation and 3D reconstruction. In this paper, we advance state-of-the-art in
neural radiance fields (NeRFs) to enable physics-informed dense depth
estimation and color correction. Our proposed method, WaterNeRF, estimates
parameters of a physics-based model for underwater image formation, leading to
a hybrid data-driven and model-based solution. After determining the scene
structure and radiance field, we can produce novel views of degraded as well as
corrected underwater images, along with dense depth of the scene. We evaluate
the proposed method qualitatively and quantitatively on a real underwater
dataset.

Robotics;Computer Vision and Pattern Recognition;Image and Video Processing