Rendering (Session 1)
Second-Order Approximation for Variance Reduction in Multiple Importance Sampling
Article first published online: 25 NOV 2013
© 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and John Wiley & Sons Ltd.
Computer Graphics Forum
Volume 32, Issue 7, pages 131–136, October 2013
How to Cite
Lu, H., Pacanowski, R. and Granier, X. (2013), Second-Order Approximation for Variance Reduction in Multiple Importance Sampling. Computer Graphics Forum, 32: 131–136. doi: 10.1111/cgf.12220
- Issue published online: 25 NOV 2013
- Article first published online: 25 NOV 2013
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.