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We fit a low-dimensional\nphysically-based outdoor illumination model to the skies in these panoramas\ngiving us a compact set of parameters (including sun position, atmospheric\nconditions, and camera parameters). We extract limited field-of-view images\nfrom the panoramas, and train a CNN with this large set of input image--output\nlighting parameter pairs. Given a test image, this network can be used to infer\nillumination parameters that can, in turn, be used to reconstruct an outdoor\nillumination environment map. We demonstrate that our approach allows the\nrecovery of plausible illumination conditions and enables photorealistic\nvirtual object insertion from a single image. 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