Image Analysis Using Multigrid Relaxation Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Computation of Visible-Surface Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to algorithms
Shape from shading
Height and gradient from shading
International Journal of Computer Vision
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algebraic Approach to Surface Reconstruction from Gradient Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Combinatorial Surface Integration
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
3D face reconstructions from photometric stereo using near infrared and visible light
Computer Vision and Image Understanding
Surface-from-Gradients without Discrete Integrability Enforcement: A Gaussian Kernel Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the range of surface reconstructions from a gradient field?
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Robust 3D face capture using example-based photometric stereo
Computers in Industry
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We describe a fast and robust gradient integration method that computes scene depths (or heights) from surface gradient (or surface normal) data such as would be obtained by photometric stereo or interferometry. Our method allows for uncertain or missing samples, which are often present in experimentally measured gradient maps; for sharp discontinuities in the scene's depth, e.g. along object silhouette edges; and for irregularly spaced sampling points. To accommodate these features of the problem, we use an original and flexible representation of slope data, the weight-delta mesh. Like other state of the art solutions, our algorithm reduces the problem to a system of linear equations that is solved by Gauss-Seidel iteration with multi-scale acceleration. Its novel key step is a mesh decimation procedure that preserves the connectivity of the initial mesh. Tests with various synthetic and measured gradient data show that our algorithm is as accurate and efficient as the best available integrators for uniformly sampled data. Moreover our algorithm remains accurate and efficient even for large sets of weakly-connected instances of the problem, which cannot be efficiently handled by any existing algorithm.