2026-04-13T01:27:23Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/94532024-06-18T12:15:17Zcom_2072_452955com_2072_2054col_2072_453069

00925njm 22002777a 4500 dc Szirmay-Kalos, László author 2005 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 re°ected rays. Comparing it with the classical path tracing approach we concluded that the new method reduced the variance signi¯cantly http://hdl.handle.net/10256/9453 Montecarlo, Mètode de Monte Carlo method Estimació, Teoria de l' Estimation theory Rutes aleatòries (Matemàtica) Random walks Go with the Winners Strategy in Path Tracing