Vector-Valued Monte Carlo Integration Using Ratio Control Variates
ACM Transactions on Graphics (Proceedings of SIGGRAPH 2025)
Haolin Lu1,2, Delio Vicini3, Wesley Chang1, and Tzu-Mao Li1
1 University of California San Diego
2 Max Planck Institute for Informatics
3 Google Inc.
Description
Variance reduction techniques for Monte Carlo integration are typically designed for scalar-valued integrands, even though many rendering and inverse rendering tasks actually involve vector-valued integrands. We show that ratio control variates, compared to conventional difference control variates, can significantly reduce the error of vector-valued integration with minimal overhead.
BibTeX
@inproceedings{Lu2025VMC,
author = {Lu, Haolin and Vicini, Delio and Chang, Wesley and Li, Tzu-Mao},
title = {Vector-Valued Monte Carlo Integration Using Ratio Control Variates},
year = {2025},
issue_date = {August 2025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {44},
number = {4},
journal = {ACM Trans. Graph.},
month = aug,
numpages = {16}
}