Dense photometric stereo using tensorial belief propagation

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 132 - 139 vol. 1
• Main Authors: Kam-Lun Tang, Chi-Keung Tang, Tien-Tsin Wong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Acoustic reflection; Belief propagation; Handheld computers; Image reconstruction; Markov random fields; Message passing; Noise robustness; Optical reflection; Photometry; Stereo image processing
• DOI: 10.1109/CVPR.2005.124
• ISBN: 0769523722; 9780769523729
• ISSN: 1063-6919

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.