Shape from shading (SFS) deals with the recovery of 3D shape from a single monocular image. This problem was formally introduced by Horn in the early 1970s. Since then it has received considerable attention, and several efforts have been made to improve the shape recovery. In this thesis, we present a fast SFS algorithm, which is a purely local method and is highly parallelizable. In our approach, we first use the discrete approximations for surface gradients, p and q, using finite differences, then linearize the reflectance function in depth, Z ( x , y), instead of p and q. This method is simple and efficient, and yields better results for images with central illumination or low-angle illumination. Furthermore, our method is more general, and can be applied to either Lambertian surfaces or specular surfaces. The algorithm has been tested on several synthetic and real images of both Lambertian and specular surfaces, and good results have been obtained. However, our method assumes that the input image contains only single object with uniform albedo values, which is commonly assumed in most SFS methods. Our algorithm performs poorly on images with nonuniform albedo values and produces incorrect shape for images containing objects with scale ambiguity, because those images violate the basic assumptions made by our SFS method. Therefore, we extended our method for images with nonuniform albedo values. We first estimate the albedo values for each pixel, and segment the scene into regions with uniform albedo values. Then we adjust the intensity value for each pixel by dividing the corresponding albedo value before applying our linear shape from shading method. This way our modified method is able to deal with nonuniform albedo values. When multiple objects differing only in scale are present in a scene, there may be points with the same surface orientation but different depth values. No existing SFS methods can solve this kind of ambiguity directly. We also present a new approach to deal with images containing multiple objects with scale ambiguity. A depth estimate is derived from patches using a minimum downhill approach and re-aligned based on the background information to get the correct depth map. Experimental results are presented for several synthetic and real images. Finally, this thesis also investigates the problem of the discrete approximation under perspective projection. The straightforward finite difference approximation for surface gradients used under orthographic projection is no longer applicable here. because the image position components are in fact functions of the depth. In this thesis, we provide a direct solution for the discrete approximation under perspective projection. The surface gradient is derived mathematically by relating the depth value of the surface point with the depth value of the corresponding image point. We also demonstrate how we can apply the new discrete approximation to a more complicated and realistic reflectance model for SFS problem.


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Graduation Date





Shah, Murbarak


Doctor of Philosophy (Ph.D.)


College of Arts and Sciences


Department of Computer Science

Degree Program

Computer Science






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Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)



Contributor (Linked data)

Shah, Mubarak [VIAF]

Shah, Mubarak [LC]

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