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Digitisation of Impasto and Gloss in Oil Paintings via Spatially Varying Bidirectional Reflectance Distribution Function Acquisition.

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Názov: Digitisation of Impasto and Gloss in Oil Paintings via Spatially Varying Bidirectional Reflectance Distribution Function Acquisition.
Autori: Yang, Chih1 (AUTHOR) zemtice@gmail.com, Lin, Tzung‐Han2 (AUTHOR) thl@mail.ntust.edu.tw
Zdroj: Computer Graphics Forum. Nov2025, p1. 15p. 12 Illustrations.
Predmety: DIGITIZATION, PHOTOMETRIC stereo, DIGITAL technology, REFLECTANCE, PAINTING, RADIANCE, DEEP learning, SURFACE texture
Abstrakt: The growth of information technology and the Internet has increased the demand for online art exhibitions. As the digitisation of artworks often requires highly customised equipment and techniques, this study proposes a practical method for obtaining spatially varying bidirectional reflectance distribution function parameters for oil paintings with rich impasto and varying gloss. We combined the photometric stereo algorithm with a deep learning model, which was trained based on real oil painting samples. The proposed method surpasses current inverse rendering and pure deep learning methods that are limited to specific materials or synthetic data. Our system effectively reproduced the nonhomogeneous nature of oil paintings by capturing normal vectors, albedo, roughness, and specular intensity for each pixel. This approach provides a practical solution for digitising oil paintings, enabling the reproduction of impastos and glossy appearances in virtual environments. [ABSTRACT FROM AUTHOR]
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Abstrakt:The growth of information technology and the Internet has increased the demand for online art exhibitions. As the digitisation of artworks often requires highly customised equipment and techniques, this study proposes a practical method for obtaining spatially varying bidirectional reflectance distribution function parameters for oil paintings with rich impasto and varying gloss. We combined the photometric stereo algorithm with a deep learning model, which was trained based on real oil painting samples. The proposed method surpasses current inverse rendering and pure deep learning methods that are limited to specific materials or synthetic data. Our system effectively reproduced the nonhomogeneous nature of oil paintings by capturing normal vectors, albedo, roughness, and specular intensity for each pixel. This approach provides a practical solution for digitising oil paintings, enabling the reproduction of impastos and glossy appearances in virtual environments. [ABSTRACT FROM AUTHOR]
ISSN:01677055
DOI:10.1111/cgf.70295