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Lighting Estimation in Indoor Environments from Low-Quality Images
Natalia Neverova, Damien Muselet, and Alain Trémeau
Laboratoire Hubert Curien – UMR CNRS 5516, University Jean Monnet, Rue du Professeur Benoît Lauras 18, 42000, Saint-Étienne, Francenatalia.neverova@etu.univ-st-etienne.fr
damien.muselet@univ-st-etienne.fr
alain.tremeau@univ-st-etienne.fr
http://laboratoirehubertcurien.fr
Abstract. Lighting conditions estimation is a crucial point in many applications. In this paper, we show that combining color images with corresponding depth maps (provided by modern depth sensors) allows to improve estimation of positions and colors of multiple lights in a scene. Since usually such devices provide low-quality images, for many steps of our framework we propose alternatives to classical algorithms that fail when the image quality is low. Our approach consists in decomposing an original image into specular shading, diffuse shading and albedo. The two shading images are used to render different versions of the original image by changing the light configuration. Then, using an optimization process, we find the lighting conditions allowing to minimize the difference between the original image and the rendered one.
Keywords: light estimation, depth sensor, color constancy
LNCS 7584, p. 380 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012