Reflectance transformation imaging (RTI) is a multilight-based imaging technique that can provide relevant information on both local microgeometry and visual appearance of a studied surface. The pixelwise angular reflectance is modeled to allow the relighting of the surface appearance under any arbitrary light direction. The primary methods used to model this local reflectance in each pixel are polynomial texture mapping, hemispherical harmonics, and, more recently, discrete modal decomposition. For all these methods, a uniform distribution of the light positions over the hemisphere is an implicit hypothesis. However, it is not always possible to satisfy this condition in practice. As a result of this nonuniform distribution, several artifacts can affect the reconstruction and alter the quality of the visual appearance assessment. To address this issue, we propose a methodology consisting of 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 used afterward to weight the least square regression and thus correct the contributions of each image to the RTI acquisition. This methodology is applied on three surfaces with visual singularities, which present different reflectance responses. From the presented results, it can be concluded that it is necessary to take into account this nonuniformity in order not to alter the quality of reconstruction/relighting from RTI data and the subsequent inspection task.
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.