Shape from shading
Generalization of the Lambertian model and implications for machine vision
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination for computer generated pictures
Communications of the ACM
Optimal Algorithm for Shape from Shading and Path Planning
Journal of Mathematical Imaging and Vision
A New Perspective [on] Shape-from-Shading
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Shape-from-Shading Under Perspective Projection
International Journal of Computer Vision
Shape from Shading: A Well-Posed Problem?
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A New Formulation for Shape from Shading for Non-Lambertian Surfaces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A fast marching formulation of perspective shape from shading under frontal illumination
Pattern Recognition Letters
Perspective Shape from Shading with Non-Lambertian Reflectance
Proceedings of the 30th DAGM symposium on Pattern Recognition
Some remarks on perspective shape-from-shading models
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Fast marching method for generic shape from shading
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Spherical surface parameterization for perspective shape from shading
Pattern Recognition Letters
Perspective Shape from Shading: Ambiguity Analysis and Numerical Approximations
SIAM Journal on Imaging Sciences
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Shape from Shading (SfS) is one of the oldest problems in image analysis that is modelled by partial differential equations (PDEs). The goal of SfS is to compute from a single 2-D image a reconstruction of the depicted 3-D scene. To this end, the brightness variation in the image and the knowledge of illumination conditions are used. While the quality of models has reached maturity, there is still the need for efficient numerical methods that enable to compute sophisticated SfS processes for large images in reasonable time. In this paper we address this problem. We consider a so-called Fast Marching (FM) scheme,which is one of the most efficient numerical approaches available. However, the FM scheme is not trivial to use for modern non-linear SfS models. We show how this is done for a recent SfS model incorporating the non-Lambertian reflectance model of Phong. Numerical experiments demonstrate that --- without compromising quality --- our FM scheme is two orders of magnitude faster than standard methods.