Estimation of Illuminant Direction, Albedo, and Shape from Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking level sets by level sets: a method for solving the shape from shading problem
Computer Vision and Image Understanding
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Constraints on Data-Closeness and Needle Map Consistency for Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Algorithm for Shape from Shading and Path Planning
Journal of Mathematical Imaging and Vision
Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from Shading and Viscosity Solutions
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
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
Surface reconstruction via helmholtz reciprocity with a single image pair
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A solution to illumination direction estimation of a shaded image: Genetic algorithm
Image and Vision Computing
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We present a shape-from-shading approach for oblique lighting with accuracy enhancement by light direction optimization. Based on an application of the Jacobi iterative method to the consistency between the reflectance map and image, four surface normal approximations are introduced and the resulting four iterative relations are combined as constraints to get an iterative relation. The matrix that converts the shading information to the depth is modified so as to be uniform over the whole image region, making the iteration stable and, as a result, the resulting shape more accurate. Then, to enhance the accuracy, the light direction is optimized for slant angle using two criteria based on the initial boundary value and the rank of the converting matrix. The method is examined using synthetic and real images to show that it is superior to the current state-of-the-art methods and that it is effective for oblique light direction whose slant angle ranges from 55 to 75 degrees.