Automating RTI: Automatic light direction detection and correcting non-uniform lighting for more accurate surface normals

Abstract Reflectance Transformation Imaging (RTI) (Malzbender et al., 2001) is a photometric stereo technique that enables the interactive relighting of the object of interest from novel lighting directions, and an estimation of surface topography through the calculation of surface normal vectors. We propose a novel, fully automated technique for correcting common lighting errors in RTI and markedly improve the accuracy of surface normal estimation, as well as increasing the legibility of low relief surface variations. This moves RTI from the qualitative domain (e.g. enabling the reading of weathered inscriptions) into the quantitative domain of computer vision. RTI assumes only light direction, and not received intensity, changes as the object is imaged. Like other authors we show that this assumption is false and propose a novel method to correct for it. However, we estimate the lighting directions automatically, unlike other proposed correction techniques. Our method also requires no calibration equipment, meaning it can be easily retrofitted to any existing stack of RTI photographs. We increase the simplicity of the standard highlight RTI method by automatically detecting lighting directions and maintain its appeal to non-imaging professionals.