Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866913700570464256 |
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| author | Rodriguez-Pardo, Carlos Pascual-Hernandez, David Rodriguez-Vazquez, Javier Lopez-Moreno, Jorge Garces, Elena |
| author_facet | Rodriguez-Pardo, Carlos Pascual-Hernandez, David Rodriguez-Vazquez, Javier Lopez-Moreno, Jorge Garces, Elena |
| contents | Flatbed scanners have emerged as promising devices for high-resolution, single-image material capture. However, existing approaches assume very specific conditions, such as uniform diffuse illumination, which are only available in certain high-end devices, hindering their scalability and cost. In contrast, in this work, we introduce a method inspired by intrinsic image decomposition, which accurately removes both shading and specularity, effectively allowing captures with any flatbed scanner. Further, we extend previous work on single-image material reflectance capture with the estimation of opacity and transmittance, critical components of full material appearance (SVBSDF), improving the results for any material captured with a flatbed scanner, at a very high resolution and accuracy |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14462 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner Rodriguez-Pardo, Carlos Pascual-Hernandez, David Rodriguez-Vazquez, Javier Lopez-Moreno, Jorge Garces, Elena Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 68T07 (Primary) 68T45, 68U10, 68U05 (Secondary) I.4.0; I.2.6; I.3.0 Flatbed scanners have emerged as promising devices for high-resolution, single-image material capture. However, existing approaches assume very specific conditions, such as uniform diffuse illumination, which are only available in certain high-end devices, hindering their scalability and cost. In contrast, in this work, we introduce a method inspired by intrinsic image decomposition, which accurately removes both shading and specularity, effectively allowing captures with any flatbed scanner. Further, we extend previous work on single-image material reflectance capture with the estimation of opacity and transmittance, critical components of full material appearance (SVBSDF), improving the results for any material captured with a flatbed scanner, at a very high resolution and accuracy |
| title | Single-image Reflectance and Transmittance Estimation from Any Flatbed Scanner |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 68T07 (Primary) 68T45, 68U10, 68U05 (Secondary) I.4.0; I.2.6; I.3.0 |
| url | https://arxiv.org/abs/2502.14462 |