  Neural radiance fields (NeRFs) have demonstrated state-of-the-art performance
for 3D computer vision tasks, including novel view synthesis and 3D shape
reconstruction. However, these methods fail in adverse weather conditions. To
address this challenge, we introduce DehazeNeRF as a framework that robustly
operates in hazy conditions. DehazeNeRF extends the volume rendering equation
by adding physically realistic terms that model atmospheric scattering. By
parameterizing these terms using suitable networks that match the physical
properties, we introduce effective inductive biases, which, together with the
proposed regularizations, allow DehazeNeRF to demonstrate successful multi-view
haze removal, novel view synthesis, and 3D shape reconstruction where existing
approaches fail.
