View-Consistent Intrinsic Decomposition for Stereoscopic Images

Abstract

In this paper, we focus on the intrinsic image decomposition problem for stereoscopic image pairs. The existing methods cannot be applied directly to decompose stereoscopic images, as it often produces inconsistent reflectance (albedo) and 3D artifacts after the decomposition. We propose a straightforward yet effective framework that enables a high-quality decomposition for stereoscopic pairs. First, retinex-based constraints are employed to coarsely classify the observed image gradients into two categories that are caused by reflectance changes and illumination variations, respectively. Second, reflectance-consistent constraints are added to control the reflectance consistency between the left and right views. Since this problem is highly ill-posed, we further analyze local and non-local image textures regularized by super-pixels within and across two views to reduce reflectance ambiguity. Lastly, absolute-scale constraints are employed to normalize the decomposition results. Extensive experiments on the real-world stereoscopic images and synthetic stereoscopic images reveal that our method can readily achieve high-quality decomposition performance.