Reflectance Transformation Imaging is a technique that provides a digital and useful representation of an object through photometric and geometric local assessment of the surface. RTI technique consists in acquiring a sequence of images from a fixed observation position while varying the direction of the light source around the observed object. Thanks to a further reconstruction process, the continuous angular reflectance for each pixel can be computed from the set of discrete acquisitions and rendered interactively. Currently, the most used mathematical functions that allow this reconstruction from RTI’s acquisitions are Polynomial Texture Mapping (PTM), a method based on Hemispherical Harmonics (HSH) and most recently the Discrete Modal Decomposition (DMD). For these three approaches, a uniform spatial distribution of light sources is an implicit hypothesis. In practice, it is often not possible to achieve this uniform spatial distribution due to intrinsic limitations in systems or in the acquisition conditions. It is then necessary to take into account this nonuniformity in order to avoid artifacts that could alter modelling and subsequent visual rendering. To address this issue, we propose a methodology consisting in the estimation of the local density of the lighting directions used during RTI acquisition. These values are then used to generate a weight for each light position enabling to correct its contribution in the regression performed during the fitting.