This paper proposes a new random walk strategy that minimizes the variance of the estimate
using statistical estimations of local and global features of the scene. Based on the local and
global properties, the algorithm decides at each point whether a Russian-roulette like random
termination is worth performing, or on the contrary, we should split the path into several child
paths. In this sense the algorithm is similar to the go-with-the-winners strategy invented in general
Monte Carlo context. However, instead of establishing thresholds to make decisions, we compute
the number of child paths on a continuous level and show that Russian roulette can be interpreted
as a kind of splitting using fractional number of children. The new method is built into a path
tracing algorithm, and a minimum cost heuristic is proposed for choosing the number of rejected
rays. Comparing it with the classical path tracing approach we concluded that the new method
reduced the variance significantly