Statistically based reflection model for rough surfaces

Yinlong Sun

doi:10.1117/12.477358

1 July 2003 Statistically based reflection model for rough surfaces

Yinlong Sun

Proceedings Volume 5016, Computational Imaging; (2003) https://doi.org/10.1117/12.477358
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

Abstract

Modeling light reflection from rough surfaces is an essential problem in computer graphics, computational vision, and multispectral imaging. Existing methods commonly separate the total reflection into diffuse and specular components, but this leads to the nonphysical arbitrariness in choosing the relative weights for the two components. There also lacks a sufficient model for the self-shadowing effect, which is important for rough surfaces. To eliminate these drawbacks, we propose a new reflection model entirely using physical parameters. The surfaces are assumed homogeneous, isotropic, and microscopically smooth, and their height probability densities are assumed Gaussian. Thus we derive the one-bounce reflection through Fresnel coefficient, self-shadowing factor, and probability function for surface orientation. The shadowing factor is calculated analytically from the statistical properties of a rough surface, including the height probability density and correlation function, and it agrees well with numerical simulation. Since all involved parameters in this model are physical, it can be easily verified with measurement. Besides, as a single term, this model generates a sharp specular highlight when a surface is smooth and shows diffuse behavior when the surface is rough. This advantage will be shown through rendered images.

Citation Download Citation

Yinlong Sun "Statistically based reflection model for rough surfaces", Proc. SPIE 5016, Computational Imaging, (1 July 2003); https://doi.org/10.1117/12.477358

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available