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Updated: Jul 7, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
Published on: May 20, 2013
Diffuse and specular reflectance from rough surfaces.
B van Ginneken1, M Stavridi, J J Koenderink
1Helmholtz Institute, Utrecht University, Princetonplein 5, Utrecht, The Netherlands.
This study introduces a new reflection model for rough surfaces, accounting for both diffuse and specular light. The model accurately predicts surface texture under varying illumination and viewing conditions.
Area of Science:
- Optics
- Computer Graphics
- Material Science
Background:
- Existing reflection models often simplify rough surfaces as Lambertian, neglecting roughness effects on diffuse reflection.
- Accurate modeling of surface reflection is crucial for realistic rendering in computer graphics and analysis in machine vision.
Purpose of the Study:
- To develop a novel reflection model for isotropic rough surfaces incorporating both specular and diffuse components.
- To account for the influence of surface roughness on diffuse reflection and include geometrical effects like masking and shadowing.
Main Methods:
- A reflection model was developed assuming a normal distribution of surface heights.
- Key parameters include root-mean-square (rms) slope, albedo, and the diffuse-specular reflection balance.
- The model was validated against goniophotometric measurements of tile and brick samples.
Main Results:
- The proposed model demonstrated a good fit with experimental data across samples with diverse reflection properties.
- Laser profilometer measurements confirmed the realism of the assumed surface height distribution and rms slope.
- The model successfully incorporates the effect of roughness on diffuse reflection, moving beyond a simple Lambertian approximation.
Conclusions:
- The developed reflection model provides a more realistic approximation of surface reflection characteristics.
- It has potential applications in machine vision and computer graphics for surface analysis and rendering.
- The model can predict surface texture variations based on illumination and viewing angles.
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