Dense Photometric Stereo: A Markov Random Field Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient belief propagation for higher-order cliques using linear constraint nodes
Computer Vision and Image Understanding
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
Estimating scene flow using an interconnected patch surface model with belief-propagation inference
Computer Vision and Image Understanding
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We address the normal reconstruction problem by photometric stereo using a uniform and dense set of photometric images captured at fixed viewpoint. Our method is robust to spurious noises caused by highlight and shadows and non-Lambertian reflections. To simultaneously recover normal orientations and preserve discontinuities, we model the dense photometric stereo problem into two coupled Markov Random Fields (MRFs): a smooth field for normal orientations, and a spatial line process for normal orientation discontinuities. We propose a very fast tensorial belief propagation method to approximate the maximum a posteriori (MAP) solution of the Markov network. Our tensor-based message passing scheme not only improves the normal orientation estimation from one of discrete to continuous, but also reduces storage and running time drastically. A convenient handheld device was built to collect a scattered set of photometric samples, from which a dense and uniform set on the lighting direction sphere is obtained. We present very encouraging results on a wide range of difficult objects to show the efficacy of our approach.