The Community for Technology Leaders
RSS Icon
Subscribe
San Diego, CA, USA
Sept. 21, 2005 to Sept. 23, 2005
ISBN: 0-7695-2372-2
pp: 132-139
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
ABSTRACT
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.
INDEX TERMS
null
CITATION
Kam-Lun Tang, Chi-Keung Tang, Tien-Tsin Wong, "Dense Photometric Stereo Using Tensorial Belief Propagation", CVPR, 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2005, pp. 132-139, doi:10.1109/CVPR.2005.124
5 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool