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In this work, we focus on outdoor lighting estimation by aggregating individual noisy estimates from images, exploiting the rich image information from wide-angle cameras and/or temporal image sequences. Photographs inherently encode information about the lighting of the scene in the form of shading and shadows. Recovering the lighting is an inverse rendering problem and as that ill-posed. Recent research based on deep neural networks has shown promising results for estimating light from a single image, but with shortcomings in robustness. |
In our work, we go in a similar direction as we robustly estimate the global sun direction and other lighting parameters (Lalonde & Matthews, 2014) by fusing estimates both from the spatial and temporal domains. |
