The paper presents a new mutation strategy for the Metropolis light transport algorithm, which works in the space of uniform random numbers used to build up paths. Thus instead of mutating directly in the path space, mutations are realized in the infinite dimensional unit cube of pseudo-random numbers and these points are transformed to the path space according to BRDF sampling, light source sampling and Russian roulette. This transformation makes the integrand and the importance function flatter and thus increases the acceptance probability of the new samples in the Metropolis algorithm. Higher acceptance ratio, in turn, reduces the correlation of the samples, which increases the speed of convergence. When mutations are calculated, a new random point is selected usually in the neighborhood of the previous one, but according to our proposition called ``large steps'', sometimes an independent point is obtained. Large steps greatly reduce the start-up bias and guarantee the ergodicity of the process. Due to the fact that some samples are generated independently of the previous sample, this method can also be considered as a combination of the Metropolis algorithm with a classical random walk. Metropolis light transport is good in rendering bright image areas but poor in dark sections since it allocates samples proportionally to the pixel luminance. Conventional random walks, on the other hand, have the same performace everywhere, thus they are poorer than Metropolis method in bright areas, but are better at dark sections. In order to keep the merits of both approaches, we use multiple importance sampling to combine their results. The resulting scheme is robust, efficient, but most importantly, is easy to implement and to combine with an available random-walk algorithm.
Metropolis sampling, MLT algorithm, importance sampling.