A Theory of Photometric Stereo for a Class of Diffuse Non-Lambertian Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the parameters of an illumination model using photometric stereo
Graphical Models and Image Processing
Extracting the Shape and Roughness of Specular Lobe Objects Using Four Light Photometric Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense Photometric Stereo Using Tensorial Belief Propagation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Dense Photometric Stereo Using a Mirror Sphere and Graph Cut
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Shapelets Correlated with Surface Normals Produce Surfaces
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Passive Photometric Stereo from Motion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Shape and materials by example: a photometric stereo approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Median Photometric Stereo as Applied to the Segonko Tumulus and Museum Objects
International Journal of Computer Vision
Photometric stereo from maximum feasible Lambertian reflections
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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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.