Dense Photometric Stereo: A Markov Random Field Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Advances in Signal Processing
Shape from Shading Using Probability Functions and Belief Propagation
International Journal of Computer Vision
Median Photometric Stereo as Applied to the Segonko Tumulus and Museum Objects
International Journal of Computer Vision
Dense photometric stereo by expectation maximization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
International Journal of Computer Vision
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We present a surprisingly simple system that performs robust normal reconstruction by dense photometric stereo, in the presence of large shadows, highlight, transparencies, complex geometry, variable attenuation in light intensity and inaccurate light directions. Our system consists of a mirror sphere, a spotlight and a DV camera only. Using this, we infer a dense set of unbiased but noisy photometric data uniformly distributed on the light direction sphere. We use this dense set to derive a very robust matching cost for our MRF photometric stereo model, where the Maximum A Posteriori (MAP) solution is estimated. To aggregate support for candidate normals in the normal refinement process, we introduce a compatibility function that is translated into a discontinuity-preserving metric, thus speeding up the MAP estimation by energy minimization using graph cut. No reference object of similar material is used. We perform detailed comparison on our approach with conventional convex minimization. We show very good normals estimated from very noisy data on a wide range of difficult objects to show the robustness and usefulness of our method.