Light spatial distribution calibration based on local density estimation for Reflectance Transformation Imaging

Reflectance Transformation Imaging (RTI) is a multi-light-based imaging technique that can provide relevant information on both local micro-geometry and visual appearance of a studied surface. The local angular reflectance is modelled to allow the relighting of the surface appearance under any arbitrary light direction. The methods used to model the local reflectance of each pixel are mainly PTM (2nd order polynomial functions), HSH (Hemispherical Harmonics) and more recently DMD (Dissrete Modal Decomposition). For all these methods, a uniform distribution of the light positions over the hemisphere is an implicit hypothesis. However, it's impossible to satisfy this condition in practice. As a result of this non-homogeneous distribution, several artifacts can affect the reconstruction and alter the quality of the visual appearance assessment. To address this issue, we proposed a methodology consisting in the estimation of the spatial distribution of the lighting directions used during RTI acquisitions, based on a local density estimation. These local density values are then used to weight the Least Squares regression, and thus to correct the contributions of each image of the RTI acquisitions. This methodology is applied on two metallic surfaces with visual singularities. From presented results, it can be concluded that it is necessary to take into account this non-uniformity in order not to alter the quality of RTI data and subsequent inspection tasks.