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Inverse rendering of outdoor scenes from unconstrained image collections is achallenging task, particularly illumination/albedo ambiguities and occlusion ofthe illumination environment (shadowing) caused by geometry. However, there aremany cues in an image that can aid in the disentanglement of geometry, albedoand shadows. We exploit the fact that any sky pixel provides a directmeasurement of distant lighting in the corresponding direction and, via aneural illumination prior, a statistical cue as to the remaining illuminationenvironment. We also introduce a novel `outside-in' method for computingdifferentiable sky visibility based on a neural directional distance function.This is efficient and can be trained in parallel with the neural scenerepresentation, allowing gradients from appearance loss to flow from shadows toinfluence estimation of illumination and geometry. Our method estimateshigh-quality albedo, geometry, illumination and sky visibility, achievingstate-of-the-art results on the NeRF-OSR relighting benchmark. Our code andmodels can be found https://github.com/JADGardner/neusky

The Sky's the Limit: Re-lightable Outdoor Scenes via a Sky-pixel Constrained Illumination Prior and Outside-In Visibility