Unifying single-image lighting estimation

We present a method for automatically estimating the lighting conditions from a single image. As opposed to most previous works which proposed methods that deal with individual aspects of the problem (e.g. indoors vs outdoors, parametric vs non-parametric), the proposed method unifies these ideas into a single, coherent framework. Our method will automatically estimate both parametric (individual light sources) and non-parametric (environment maps) lighting representations from both indoor and outdoor images. To this end, we will develop a novel deep learning architecture to automatically learn the mapping between an input image to its lighting conditions in an end-to-end fashion, by relying on a large dataset of indoor and outdoor high dynamic range images for training. The method will be quantitatively evaluated and compared to the most relevant approaches from the literature.

Intern: 
Henrique Weber
Superviseur universitaire: 
Jean-Francois Lalonde
Province: 
Quebec
Université: 
Partner University: 
Programme: