  Estimating a scene's lighting is a very important task when compositing
synthetic content within real environments, with applications in mixed reality
and post-production. In this work we present a data-driven model that estimates
an HDR lighting environment map from a single LDR monocular spherical panorama.
In addition to being a challenging and ill-posed problem, the lighting
estimation task also suffers from a lack of facile illumination ground truth
data, a fact that hinders the applicability of data-driven methods. We approach
this problem differently, exploiting the availability of surface geometry to
employ image-based relighting as a data generator and supervision mechanism.
This relies on a global Lambertian assumption that helps us overcome issues
related to pre-baked lighting. We relight our training data and complement the
model's supervision with a photometric loss, enabled by a differentiable
image-based relighting technique. Finally, since we predict spherical spectral
coefficients, we show that by imposing a distribution prior on the predicted
coefficients, we can greatly boost performance. Code and models available at
https://vcl3d.github.io/DeepPanoramaLighting.
