The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from shading
Shape from shading
Numerical shape from shading and occluding boundaries
Shape from shading
Photometric method for determining surface orientation from multiple images
Shape from shading
Height and gradient from shading
International Journal of Computer Vision
Integrability disambiguates surface recovery in two-image photometric stereo
International Journal of Computer Vision
Estimation of Illuminant Direction, Albedo, and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface curvature and shape reconstruction from unknown multiple illumination and integrability
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Review
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Optimal Algorithm for Shape from Shading and Path Planning
Journal of Mathematical Imaging and Vision
Darboux smoothing for shape-from-shading
Pattern Recognition Letters
Shape-from-Shading Under Perspective Projection
International Journal of Computer Vision
Dense Photometric Stereo Using a Mirror Sphere and Graph Cut
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Implementing belief propagation in neural circuits
Neurocomputing
Approximate inference and constrained optimization
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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Shape-from-shading (SFS) aims to reconstruct the three-dimensional shape of an object from a single shaded image. This article proposes an improved framework based on belief propagation for computing SFS. The implementation of the well-known brightness, integrability and smoothness constraints inside this framework is shown.We implement the constraints as probability density functions. For example, the brightness constraint is a two-dimensional probability density function that relates all possible surface gradients at a pixel to their probability given the pixel intensity. A straightforward extension of the framework to photometric stereo is presented, where multiple images of the same scene taken under different lighting conditions are available.The results are promising, especially since the solution is obtained by iteratively applying simple operations on a regular grid of points. The presented framework therefore can be implemented in parallel and is a reasonably likely biological scheme.