RenderStorm: a Monte Carlo illumination testbed for virtual environment

Chun-hong Liu; Qing Xu; Song Zhang

doi:10.1117/12.561228

19 March 2004 RenderStorm: a Monte Carlo illumination testbed for virtual environment

Chun-hong Liu, Qing Xu, Song Zhang

Proceedings Volume 5444, Fourth International Conference on Virtual Reality and Its Applications in Industry; (2004) https://doi.org/10.1117/12.561228
Event: Fourth International Conference On Virtual Reality and Its Applications in Industry, 2003, Tianjin, China

Abstract

With the fast development of Monte Carlo global illumination, many new ideas are put forward and needed to be tested whether it is effective. A general and efficient testbed that can provide all kinds of Monte Carlo global illumination algorithms is in great demand. In this paper, we propose a Monte Carlo global illumination testbed for virtual environment -- RenderStorm, which is of highly modular architecture, good expansibility and suitability to synthesize still and animated images. The engine of this testbed is Monte Carlo path tracing technique. Based on Monte Carlo path tracing, it can be easily used for realizing a lot of Monte Carlo global illumination algorithms. A perfectly designed class is the distinct feature of the system. We provide the system with a lot of libraries, which can be used easily.

Citation Download Citation

Chun-hong Liu, Qing Xu, and Song Zhang "RenderStorm: a Monte Carlo illumination testbed for virtual environment", Proc. SPIE 5444, Fourth International Conference on Virtual Reality and Its Applications in Industry, (19 March 2004); https://doi.org/10.1117/12.561228

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