MATLAB toolbox for the regularized surface reconstruction from gradients

Matthew Harker; Paul O'Leary

doi:10.1117/12.2182827

30 April 2015 MATLAB toolbox for the regularized surface reconstruction from gradients

Matthew Harker, Paul O'Leary

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 95341E (2015) https://doi.org/10.1117/12.2182827
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

As Photometric Stereo is a means of measuring the gradient field of a surface, an essential step in the measurement of a surface structure is the reconstruction of a surface from its measured gradient field. Given that the surface normals are subject to noise, straightforward integration does not provide an adequate reconstruction of the surface. In fact, if the noise in the gradient can be considered to be Gaussian, the optimal reconstruction based on maximum likelihood principles is obtained by the method of least-squares. However, since the reconstruction of a surface from its gradient is an inverse problem, it is usually necessary to introduce some form of regularization of the solution. This paper describes and demonstrates the functionality of a library of MATLAB functions for the regularized reconstruction of a surface from its measured gradient field. The library of functions, entitled “Surface Reconstruction from Gradient Fields: grad2Surf Version 1.0” is available at the MATLAB file-exchange

http://www.mathworks.com/matlabcentral/fileexchange/authors/321598

The toolbox is the culmination of a number of papers on the least-squares reconstruction of a surface from its measured gradient field, regularized solutions to the problem, and real-time implementations of the algorithms.^1-4

Citation Download Citation

Matthew Harker and Paul O'Leary "MATLAB toolbox for the regularized surface reconstruction from gradients", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95341E (30 April 2015); https://doi.org/10.1117/12.2182827

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