Revisiting Outlier Rejection Approach for Non-Lambertian Photometric Stereo.

Abstract

Photometric stereo offers a single camera based approach to recover 3D information and has attracted wide range of applications in computer vision. Presence of non-Lambertian reflections in almost all the real-world objects limits the usage of the Lambertian model for surface normal vector estimation. Previous methods proposed to address such non-Lambertian phenomena employ an outlier rejection approach while more recent methods introduce BRDF models which can generate more accurate results. However, results with comparable accuracy can also be achieved by simply filtering the observed intensity values. This paper presents two novel outlier rejection techniques which attempt to identify the data which are more reliable and likely to be Lambertian. In the first technique, observed intensity values with less reliability are automatically eliminated. This reliability is determined by the responses from a newly introduced inter-relationship function. In the second technique, those photometric ratio equations which are less likely to be Lambertian are identified by observing the residue of the equations. By eliminating the data which is unreliable and likely to be non-Lambertian, surface normal vectors are more accurately estimated. Our comparative and reproducible experimental results using both real and synthetic datasets illustrate superior performance over the state-of-the-art methods, which validates our theoretical arguments presented in this paper.