Abstract
In this paper we present a novel image-based 3D surface reconstruction technique that incorporates reflectance, polarisation, and defocus information into a variational framework. Our technique is especially suited for the difficult task of 3D reconstruction of rough metallic surfaces. An error functional composed of several error terms related to the measured reflectance and polarisation properties is minimised by means of an iterative scheme in order to obtain a 3D reconstruction of the surface. This iterative algorithm is initialised with the result of depth from defocus analysis. By evaluating the algorithm on synthetic ground truth data, we show that the combined approach strongly improves the accuracy of the surface reconstruction result compared to techniques based on either reflectance or polarisation alone. Furthermore, we report 3D reconstruction results for a raw forged iron surface. A comparison of our method to independently measured ground truth data yields an accuracy of about one third of the pixel resolution.
Published Version
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