Reflectance Transformation Imaging (RTI) is a computational photographic method that captures an object’s surface shape & color and enables the interactive re-lighting of the subject from any direction. RTI model of an object is built from multiple images of it captured by a stationary camera but varying light directions. By changing the direction of the light, the respective micro-geometry of the object is highlighted. The RTI acquisition process is often long, and tedious when it is not automated. It requires expertise to define for each analysed object which are the number and the relevant lighting positions in the acquisition sequence. In this paper, we present our novel Next Best Light Position (NBLP) method to address this issue. The proposed method is based on the principle of a gradient descent allowing in an adaptive and iterative way, to automatically define the most appropriate lighting directions for the RTI acquisition of an object/surface.
In this paper, we evaluate the quality of reconstruction i.e. relighting from images obtained by a newly developed multispectral reflectance transformation imaging (MS-RTI) system. The captured MS-RTI images are of objects with different translucency and color. We use the most common methods for relighting the objects: polynomial texture mapping (PTM) and hemispherical harmonics (HSH), as well as the recent discrete model decomposition (DMD). The results show that all three models can reconstruct the images of translucent materials, with the reconstruction error varying with translucency but still in the range of what has been reported for other non-translucent materials. DMD relighted images are marginally better for the most transparent objects, while HSH- and PTM- relighted images appear to be better for the opaquer objects. The estimation of the surface normals of highly translucent objects using photometric stereo is not very accurate. Utilizing the peak of the fitted angular reflectance field, the relighting models, especially PTM, can provide more accurate estimation of the surface normals.