A Markov Random Field Approach for Dense Photometric Stereo

We present a surprisingly simple system that allows for robust normal reconstruction by photometric stereo using a uniform and dense set of photometric images captured at fixed viewpoint, in the presense of spurious noises caused by highlight, shadows and non-Lambertian reflections. Our system consists of a mirror sphere, a spotlight and a DV camera only. Using this, a dense set of unbiased but noisy photometric data that roughly distributed uniformly on the light direction sphere is produced. 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. A very fast tensorial belief propagation method is used to approximate the maximum a posteriori (MAP) solution of the Markov network. We present very encouraging results on a wide range of difficult objects to show the efficacy of our approach.