Color Subspaces as Photometric Invariants
International Journal of Computer Vision
Noise Analysis of a SFS Algorithm Formulated under Various Imaging Conditions
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Fast Shape from Shading for Phong-Type Surfaces
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Making Shape from Shading Work for Real-World Images
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Estimating Facial Reflectance Properties Using Shape-from-Shading
International Journal of Computer Vision
A new framework for grayscale and colour non-lambertian shape-from-shading
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Spherical surface parameterization for perspective shape from shading
Pattern Recognition Letters
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
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Lambert's model for diffuse reflection is a main assumption in most of shape from shading (SFS) literature. Even with this simplified model, the SFS is still a difficult problem. Nevertheless, Lambert's model has been proven to be an inaccurate approximation of the diffuse component of the surface reflectance. In this paper, we propose a new solution of the SFS problem based on a more comprehensive diffuse reflectance model: the Oren and Nayar model. In this work, we slightly modify this more realistic model in order to take into account the attenuation of the illumination due to distance. Using the modified non-Lambertian reflectance, we design a new explicit Partial Differential Equation (PDE) and then solve it using Lax-Friedrichs Sweeping method. Our experiments on synthetic data show that the proposed modeling gives a unique solution without any information about the height at the singular points of the surface. Additional results for real data are presented to show the efficiency of the proposed method . To the best of our knowledge, this is the first non-Lambertian SFS formulation that eliminates the concave/convex ambiguity which is a well known problem in SFS.