Dense Photometric Stereo by Expectation Maximization

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.