  Research on neural radiance fields (NeRFs) for novel view generation is
exploding with new models and extensions. However, a question that remains
unanswered is what happens in underwater or foggy scenes where the medium
strongly influences the appearance of objects. Thus far, NeRF and its variants
have ignored these cases. However, since the NeRF framework is based on
volumetric rendering, it has inherent capability to account for the medium's
effects, once modeled appropriately. We develop a new rendering model for NeRFs
in scattering media, which is based on the SeaThru image formation model, and
suggest a suitable architecture for learning both scene information and medium
parameters. We demonstrate the strength of our method using simulated and
real-world scenes, correctly rendering novel photorealistic views underwater.
Even more excitingly, we can render clear views of these scenes, removing the
medium between the camera and the scene and reconstructing the appearance and
depth of far objects, which are severely occluded by the medium. Our code and
unique datasets are available on the project's website.
