A data-driven photometric stereo algorithm

Abstract

Representing the complicated reflectance of non-Lambertian material is one of the difficulty in photometric stereo. This paper presents a Gaussian process based data-driven photometric stereo method to address this problem. The method makes use of Gaussian process, a non-parametric Bayesian inference model, to establish a global plausible model to represent non-linear reflectance property of non-Lambertian material based on measured bidirectional reflectance distribution functions. The statistic nature of the Gaussian process makes the method has much better flexibility and accuracy in describing the complicated reflectance of various non-Lambterian materials so that a better estimation accuracy can be achieved for a photometric stereo system. The validity of the proposed method is verified by both computer simulation and real experiment. The results show that the proposed method has much better accuracy and efficiency in estimating surface normal than other existing methods.