Perception of solid shape from shading
Biological Cybernetics
The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
Computer Vision, Graphics, and Image Processing
Solid shape
Inferring Surface Trace and Differential Structure from 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from shading
Height and gradient from shading
International Journal of Computer Vision
A viscosity solutions approach to shape-from-shading
SIAM Journal on Numerical Analysis
Singularities of Principal Direction Fields from 3-D Images
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
Shape from shading: level set propagation and viscosity solutions
International Journal of Computer Vision
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
Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction
International Journal of Computer Vision
Shading Flows and Scenel Bundles: A New Approach to Shape from Shading
ECCV '92 Proceedings of the Second European Conference on Computer Vision
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)
Terrain Analysis Using Radar Shape-from-Shading
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we describe a new shape-from-shading method. We show how the parallel transport of surface normals can be used to impose curvature consistency and also to iteratively update surface normal directions so as to improve the brightness error. We commence by showing how to make local estimates of the Hessian matrix from surface normal information. With the local Hessian matrix to hand, we develop an ''EM-like'' algorithm for updating the surface normal directions. At each image location, parallel transport is applied to the neighbouring surface normals to generate a sample of local surface orientation predictions. From this sample, a local weighted estimate of the image brightness is made. The transported surface normal which gives the brightness prediction which is closest to this value is selected as the revised estimate of surface orientation. The revised surface normals obtained in this way may in turn be used to re-estimate the Hessian matrix, and the process iterated until stability is reached. We experiment with the method on a variety of real world and synthetic data. Here we explore the properties of the fields of surface normals and the height data delivered by the method.