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
Computer Vision, Graphics, and Image Processing
Inferring Surface Trace and Differential Structure from 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface shape and curvature scales
Image and Vision Computing
Tracking level sets by level sets: a method for solving the shape from shading problem
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
Highlight Removal Using Shape-from-Shading
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Testing new variants of the Beckmann-Kirchhoff model against radiance data
Computer Vision and Image Understanding
Integrative 3D modelling of complex carving surface
Computer-Aided Design
Shape from Shading Using Probability Functions and Belief Propagation
International Journal of Computer Vision
Testing new variants of the Beckmann-Kirchhoff model against radiance data
Computer Vision and Image Understanding
Surface radiance correction for shape from shading
Pattern Recognition
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This paper describes a new surface normal smoothing process which can be used in conjunction with shape-from-shading. Rather than directly smoothing the surface normal vectors, we exert control over their directions by smoothing the field of principal curvature vectors. To do this we develop a topography sensitive smoothing process which overcomes the problems of singularities in the field of principal curvature directions at the locations of umbilics and saddles. The method is evaluated on both synthetic and real-world images.