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Neural radiance fields (NeRFs) have demonstrated state-of-the-art performancefor 3D computer vision tasks, including novel view synthesis and 3D shapereconstruction. However, these methods fail in adverse weather conditions. Toaddress this challenge, we introduce DehazeNeRF as a framework that robustlyoperates in hazy conditions. DehazeNeRF extends the volume rendering equationby adding physically realistic terms that model atmospheric scattering. Byparameterizing these terms using suitable networks that match the physicalproperties, we introduce effective inductive biases, which, together with theproposed regularizations, allow DehazeNeRF to demonstrate successful multi-viewhaze removal, novel view synthesis, and 3D shape reconstruction where existingapproaches fail.

DehazeNeRF: Multiple Image Haze Removal and 3D Shape Reconstruction using Neural Radiance Fields