Title: Learning to Predict Indoor Illumination from a Single Image,
Abstract: We propose an automatic method to infer high dynamic range illumination from
a single, limited field-of-view, low dynamic range photograph of an indoor
scene. In contrast to previous work that relies on specialized image capture,
user input, and/or simple scene models, we train an end-to-end deep neural
network that directly regresses a limited field-of-view photo to HDR
illumination, without strong assumptions on scene geometry, material
properties, or lighting. We show that this can be accomplished in a three step
process: 1) we train a robust lighting classifier to automatically annotate the
location of light sources in a large dataset of LDR environment maps, 2) we use
these annotations to train a deep neural network that predicts the location of
lights in a scene from a single limited field-of-view photo, and 3) we
fine-tune this network using a small dataset of HDR environment maps to predict
light intensities. This allows us to automatically recover high-quality HDR
illumination estimates that significantly outperform previous state-of-the-art
methods. Consequently, using our illumination estimates for applications like
3D object insertion, we can achieve results that are photo-realistic, which is
validated via a perceptual user study.