San Diego, California
June 20, 2005 to June 26, 2005
Kam-Lun Tang , Hong Kong University of Science and Technology
Chi-Keung Tang , Hong Kong University of Science and Technology
Tien-Tsin Wong , Chinese University of Hong Kong
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2005.124
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.
Kam-Lun Tang, Chi-Keung Tang, Tien-Tsin Wong, "Dense Photometric Stereo Using Tensorial Belief Propagation", CVPR, 2005, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition 2005, pp. 132-139, doi:10.1109/CVPR.2005.124