Hybrid Radiosity/Monte Carlo Methods
نویسنده
چکیده
Monte Carlo methods refer to any method that uses random numbers to get an approximate answer to a problem. In computer graphics Monte Carlo techniques can be used to perform radiosity calculations and can be used in distribution ray tracing for effects such as soft shadows and motion blur. Although these notes are for a course on radiosity, they have a great deal of material from a previous Siggraph 93 global illumination course notes that also dealt with distribution ray tracing, although the radiosity material has been expanded. In these notes I will cover the basics of both Monte Carlo simulation, where a physical system is modeled, Monte Carlo integration, where random numbers are used to approximate integrals, and Quasi-Monte Carlo integration, where non-random numbers are used. This discussion will cover the general techniques, and will use global illumination problems as examples. One appeal of using Monte Carlo methods is that they are easy to design and use. However, it is not so easy to design a good Monte Carlo method, where the computation can be completed to the desired accuracy relatively quickly. Here both cleverness and some analytic skills are required. Fortunately, the analytic skills are fairly narrow in scope, so many of them can be covered in this short tutorial. A more formal discussion of Monte Carlo simulation can be found in the neutron transport literature (e.g. [47]) and an extremely current survey of Monte Carlo integration for practical applications can be found in the survey article by Spanier and Maize[48].
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