SeaThru-NeRF: Neural Radiance Fields in Scattering Media
This addresses a domain-specific problem for underwater imaging and foggy scene reconstruction, offering a novel extension to NeRFs.
The paper tackles the problem of novel view generation in underwater or foggy scenes using neural radiance fields (NeRFs), which previously ignored scattering media, and demonstrates the method by rendering photorealistic views and clearing medium effects to reconstruct occluded objects.
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.