Dense Photometric Stereo Using Tensorial Belief Propagation

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 tensorbased 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...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Kam-Lun Tang, Chi-Keung Tang, Tien-Tsin Wong
• Keywords: Computer Vision, CVPR 2005, Dense Photometric Stereo, Normal Orientation, Photometric Stereo