Illumination from Shadows

In this paper, we introduce a method for recovering an illumination distribution of a scene from image brightness inside shadows cast by an object of known shape in the scene. In a natural illumination condition, a scene includes both direct and indirect illumination distributed in a complex way, and it is often difficult to recover an illumination distribution from image brightness observed on an object surface. The main reason for this difficulty is that there is usually not adequate variation in the image brightness observed on the object surface to reflect the subtle characteristics of the entire illumination. In this study, we demonstrate the effectiveness of using occluding information of incoming light in estimating an illumination distribution of a scene. Shadows in a real scene are caused by the occlusion of incoming light, and thus analyzing the relationships between the image brightness and the occlusions of incoming light enables us to reliably estimate an illumination distribution of a scene even in a complex illumination environment. This study further concerns the following two issues that need to be addressed. First, the method combines the illumination analysis with an estimation of the reflectance properties of a shadow surface. This makes the method applicable to the case where reflectance properties of a surface are not known a priori and enlarges the variety of images applicable to the method. Second, we introduce an adaptive sampling framework for efficient estimation of illumination distribution. Using this framework, we are able to avoid unnecessarily dense sampling of the illumination and can estimate the entire illumination distribution more efficiently with a smaller number of sampling directions of the illumination distribution. To demonstrate the effectiveness of the proposed method, we have successfully tested the proposed method by using sets of real images taken in natural illumination conditions with different surface materials of shadow regions. Keywords—computer vision, physics based vision, illumination distribution estimation