Intrinsic Scene Decomposition from RGB-D images

Intrinsic Scene Decomposition from RGB-D images

Mohamed Hachama, Bernard Ghanem, and Peter Wonka
"Intrinsic Scene Decomposition from RGB-D Images"
International Conference on Computer Vision (ICCV 2015
Mohamed Hachama, Bernard Ghanem, Peter Wonka
Intrinsic Decomposition, RGB-D Images
2015

​In this paper, we address the problem of computing an intrinsic decomposition of the colors of a surface into an albedo and a shading term. The surface is reconstructed from a single or multiple RGB-D images of a static scene obtained from different views. We thereby extend and improve existing works in the area of intrinsic image decomposition. In a variational framework, we formulate the problem as a minimization of an energy composed of two terms: a data term and a regularity term. The first term is related to the image formation process and expresses the relation between the albedo, the surface normals, and the incident illumination. We use an affine shading model, a combination of a Lambertian model, and an ambient lighting term. This model is relevant for Lambertian surfaces. When available, multiple views can be used to handle view-dependent non- Lambertian reflections. The second term contains an efficient combination of  l2 and  l1-regularizers on the illumination vector field and albedo respectively. Unlike most previous approaches, especially Retinex-like techniques, these terms do not depend on the image gradient or texture, thus reducing the mixing shading/reflectance artifacts and leading to better results. The obtained non-linear optimization problem is efficiently solved using a cyclic block coordinate descent algorithm. Our method outperforms a range of state-of-the-art algorithms on ​a popular benchmark datase