Abstract
Modeling the shape and appearance of a thin film object has promising applications such as heritage-modeling and industrial inspections. In the same time, such modeling is a frontier of computer vision and contains various challenging issues to be solved. In particular, thin film colors show iridescence along the view and lighting directions and how to acquire and formalize the spectral iridescence for shape estimation. This paper aims to model the shapes and appearances of thin film objects from measured reflectance spectra. Thin film reflectance is represented by the incident angle on the object surface, the refractive index and the film thickness. First, we estimate the incident angle of a surface patch on a thin film based on monotonically increasing peak intensities. Then, we apply a characteristics strip expansion method to the peak intensity for estimating the surface normal of the patch. Based on this shape estimation, we estimate refractive index and film thickness from iridescence variance. We experimentally evaluate the accuracy of the estimated shape and estimated parameters. We also demonstrate to reconstruct appearances based on the shape and parameters.
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Kobayashi, Y., Morimoto, T., Sato, I., Mukaigawa, Y., Ikeuchi, K. (2015). Reconstructing Shape and Appearance of Thin Film Objects with Hyper Spectral Sensor. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_32
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DOI: https://doi.org/10.1007/978-3-319-16817-3_32
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