Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, limitations on the size of objects being acquired, speed, and portability. This paper presents a novel robotic arm-based system design, which we call LightBot, as well as its applications in reflectance transformation imaging (RTI) in particular. The proposed model alleviates some of the limitations observed in the case of free-form or dome-based systems. It allows the automation and reproducibility of one or a series of acquisitions adapting to a given surface in two-dimensional space.
In this paper, we propose to evaluate the quality of the reconstruction and relighting, from images acquired by Reflectance Transformation Imaging (RTI) device, of three largely used state-of-art methods, namely PTM, HSH and DMD. We evaluate these methods with regards to an objective evaluation using PSNR and SSIM as well as visual assessment through a sensory (visual) assessment, which is still today the reference in the industry. The evaluation was also carried out with regards to different sampling densities. This study allows to estimate the efficiency of these models to reproduce the aspect of the manufactured surfaces with relevant input parameters for the RTI approach. It also shows that DMD reproduces the most accurate reconstruction/relighting to an acquired measurement and that a higher sampling density don't mean necessarily a higher perceptual quality.
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