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2013 IEEE Conference on Computer Vision and Pattern Recognition (2005)
San Diego, California
June 20, 2005 to June 26, 2005
ISSN: 1063-6919
ISBN: 0-7695-2372-2
pp: 132-139
Chi-Keung Tang , Hong Kong University of Science and Technology
Tien-Tsin Wong , Chinese University of Hong Kong
Kam-Lun Tang , Hong Kong University of Science and Technology
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.
Chi-Keung Tang, Tien-Tsin Wong, Kam-Lun Tang, "Dense Photometric Stereo Using Tensorial Belief Propagation", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 01, no. , pp. 132-139, 2005, doi:10.1109/CVPR.2005.124
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