ABSTRACT
This Chapter describes a brief overview of the classic radiosity, more introductory or more in-depth coverage of the classic radiosity method can be found in the text books. The first class, called stochastic relaxation methods, is based on stochastic adaptations of classic iterative solution methods for linear systems such as the Jacobi, Gauss–-Seidel, or South well iterative methods. The solution of linear systems, such as those that occur in the classic radiosity method, is one of the earliest applications of the Monte Carlo method. The third class of Monte Carlo radiosity methods is very similar to the random walk methods for linear systems but solves the radiosity or rendering integral equation directly, rather than the radiosity linear system. The random walks of these methods are nothing but simulated photon trajectories. This chapter concludes with a discussion of how adaptive meshing, hierarchical refinement, and clustering techniques can be incorporated into Monte Carlo radiosity.
