Removing Adverse Volumetric Effects From Trained Neural Radiance Fields
Andreas L. Teigen Mauhing Yip Victor P. Hamran Vegard Skui Annette Stahl Rudolf Mester
While the use of neural radiance fields (NeRFs) in different challenging
settings has been explored, only very recently have there been any
contributions that focus on the use of NeRF in foggy environments. We argue
that the traditional NeRF models are able to replicate scenes filled with fog
and propose a method to remove the fog when synthesizing novel views. By
calculating the global contrast of a scene, we can estimate a density threshold
that, when applied, removes all visible fog. This makes it possible to use NeRF
as a way of rendering clear views of objects of interest located in fog-filled
environments. Additionally, to benchmark performance on such scenes, we
introduce a new dataset that expands some of the original synthetic NeRF scenes
through the addition of fog and natural environments. The code, dataset, and
video results can be found on our project page: https://vegardskui.com/fognerf/