Stochastic modeling of non-Lambertian surfaces for Monte Carlo computations in optical radiometry

Leonard M. Hanssen; Alexander V. Prokhorov

doi:10.1117/12.826468

1 October 2009 Stochastic modeling of non-Lambertian surfaces for Monte Carlo computations in optical radiometry

Leonard M. Hanssen, Alexander V. Prokhorov

Proceedings Volume 7427, Optical Modeling and Performance Predictions IV; 742707 (2009) https://doi.org/10.1117/12.826468
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

The accuracy of Monte Carlo calculations in optical radiometry is often limited by the reflection model employed (usually, diffuse (Lambertian), or simplified specular-diffuse). There are many cases when these models cannot adequately simulate the bidirectional reflection distribution functions (BRDFs) of real materials, which can call into question the results of the calculations. The features and the methods of modeling with the use of importance sampling for various materials are considered with a focus on an acceptance-rejection technique and on a procedural ab initio method. The Monte Carlo algorithms for modeling non-Lambertian reflection are discussed. The simulated and measured BRDFs for several materials are compared.

Citation Download Citation

Leonard M. Hanssen and Alexander V. Prokhorov "Stochastic modeling of non-Lambertian surfaces for Monte Carlo computations in optical radiometry", Proc. SPIE 7427, Optical Modeling and Performance Predictions IV, 742707 (1 October 2009); https://doi.org/10.1117/12.826468

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