  Vision in adverse weather conditions, whether it be snow, rain, or fog is
challenging. In these scenarios, scattering and attenuation severly degrades
image quality. Handling such inclement weather conditions, however, is
essential to operate autonomous vehicles, drones and robotic applications where
human performance is impeded the most. A large body of work explores removing
weather-induced image degradations with dehazing methods. Most methods rely on
single images as input and struggle to generalize from synthetic
fully-supervised training approaches or to generate high fidelity results from
unpaired real-world datasets. With data as bottleneck and most of today's
training data relying on good weather conditions with inclement weather as
outlier, we rely on an inverse rendering approach to reconstruct the scene
content. We introduce ScatterNeRF, a neural rendering method which adequately
renders foggy scenes and decomposes the fog-free background from the
participating media-exploiting the multiple views from a short automotive
sequence without the need for a large training data corpus. Instead, the
rendering approach is optimized on the multi-view scene itself, which can be
typically captured by an autonomous vehicle, robot or drone during operation.
Specifically, we propose a disentangled representation for the scattering
volume and the scene objects, and learn the scene reconstruction with
physics-inspired losses. We validate our method by capturing multi-view
In-the-Wild data and controlled captures in a large-scale fog chamber.
