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Dense Photometric Stereo by Expectation Maximization

  • Tai-Pang Wu
  • Chi-Keung Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3954)

Abstract

We formulate a robust method using Expectation Maximization (EM) to address the problem of dense photometric stereo. Previous approaches using Markov Random Fields (MRF) utilized a dense set of noisy photometric images for estimating an initial normal to encode the matching cost at each pixel, followed by normal refinement by considering the neighborhood of the pixel. In this paper, we argue that they had not fully utilized the inherent data redundancy in the dense set and that its full exploitation leads to considerable improvement. Using the same noisy and dense input, this paper contributes in learning relevant observations, recovering accurate normals and very good surface albedos, and inferring optimal parameters in an unifying EM framework that converges to an optimal solution and has no free user-supplied parameter to set. Experiments show that our EM approach for dense photometric stereo outperforms the previous approaches using the same input.

Keywords

Expectation Maximization Markov Random Field Expectation Maximization Algorithm Light Direction Photometric Stereo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tai-Pang Wu
    • 1
  • Chi-Keung Tang
    • 1
  1. 1.Vision and Graphics GroupThe Hong Kong University of Science and TechnologyClear Water BayHong Kong

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