Geometric Sample Reweighting for Monte Carlo Integration

We presentageneral sample reweighting scheme and its underlying theory for the integration of an unknown function with low dimensionality. Our method produces better results than standard weighting schemes for com-mon sampling strategies, while avoiding bias. Our main insight is to link the weight derivation to the function reconstruction process during integration. The implementation of our solution is simple and results in an improved multiple Monte Carlo rendering problems.

@Article { GE19,
  author       = "Guo, Jerry and Eisemann, Elmar",
  title        = "Geometric Sample Reweighting for Monte Carlo Integration",
  journal      = "arXiv.org",
  number       = "01809",
  volume       = "1908",
  month        = "August",
  year         = "2019",
  url          = "http://graphics.tudelft.nl/Publications-new/2019/GE19"
}