Premium
Second‐Order Approximation for Variance Reduction in Multiple Importance Sampling
Author(s) -
Lu H.,
Pacanowski R.,
Granier X.
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12220
Subject(s) - computer science , variance reduction , visibility , variance (accounting) , heuristic , overhead (engineering) , importance sampling , sampling (signal processing) , graphics , monte carlo method , computer graphics , mathematical optimization , algorithm , artificial intelligence , computer vision , computer graphics (images) , statistics , mathematics , physics , accounting , filter (signal processing) , optics , business , operating system
Monte Carlo Techniques are widely used in Computer Graphics to generate realistic images. Multiple Importance Sampling reduces the impact of choosing a dedicated strategy by balancing the number of samples between different strategies. However, an automatic choice of the optimal balancing remains a difficult problem. Without any scene characteristics knowledge, the default choice is to select the same number of samples from different strategies and to use them with heuristic techniques (e.g., balance, power or maximum). In this paper, we introduce a second‐order approximation of variance for balance heuristic. Based on this approximation, we introduce an automatic distribution of samples for direct lighting without any prior knowledge of the scene characteristics. We demonstrate that for all our test scenes (with different types of materials, light sources and visibility complexity), our method actually reduces variance in average. We also propose an implementation with low overhead for offline and GPU applications. We hope that this approach will help developing new balancing strategies.