IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Multiple Illuminants from a Single Image of Arbitrary Known Geometry
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Estimation of multiple directional light sources for synthesis of augmented reality images
Graphical Models - Special issue on Pacific graphics 2002
A Generic and Provably Convergent Shape-from-Shading Method for Orthographic and Pinhole Cameras
International Journal of Computer Vision
Sketching shiny surfaces: 3D shape extraction and depiction of specular surfaces
ACM Transactions on Applied Perception (TAP)
Shape from shading for the digitization of curved documents
Machine Vision and Applications
Numerical methods for shape-from-shading: A new survey with benchmarks
Computer Vision and Image Understanding
3D-spline reconstruction using shape from shading: Spline from shading
Image and Vision Computing
Estimation of multiple directional illuminants from a single image
Image and Vision Computing
A Multi-Image Shape-from-Shading Framework for Near-Lighting Perspective Endoscopes
International Journal of Computer Vision
Estimating illumination parameters in real space with application to image relighting
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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This paper presents a new method for the simultaneous estimation of lighting direction and shape from shading. The method estimates the shape and the lighting direction using a two step iterative process. We assume an initial (possibly incorrect) estimate of the lighting position. A stiff deformable model is then fitted to the image, assuming this lighting position. Next, a least-squares estimate of the lighting position is derived from the model using the Levenberg-Marquart method.The two steps - model fitting and lighting-position estimation - are iterated. Once the light direction has converged to a stable solution the deformable model stiffness is lowered and the model fits accurately given the lighting model. In addition, we show how the method can be used with either orthographic or perspective projection assumptions. In a variety of experiments on real and synthetic data, the method is robust to errors both to the initial light position and shape estimates.