Single-image reflectance and transmittance estimation from any flatbed scanner

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. • We introduce a generative model for digitizing materials using any flatbed scanners, capable of removing undesirable shading and specular highlights. • We expand the realism of digital replicas of material by including opacity and transmittance in the material model, both of which are key attributes for thin-layer materials like fabrics. • We provide an extensive and thorough experimentation using image-based and render aware metrics. Our results show that our method works with a wide range of scanning devices.