Continuous multiple importance sampling


Three applications of our framework to light transport simulation. We reformulate each application as a problem of combining a continuum of sampling techniques and leverage our continuous MIS (CMIS) formulation to derive an efficient weighting scheme. Based on this scheme, our practical stochastic MIS (SMIS) estimator outperforms existing state-of-the-art methods. For each image we report error in SMAPE units.


Multiple importance sampling (MIS) is a provably good way to combine a finite set of sampling techniques to reduce variance in Monte Carlo integral estimation. However, there exist integration problems for which a continuum of sampling techniques is available. To handle such cases we establish a continuous MIS (CMIS) formulation as a generalization of MIS to uncountably infinite sets of techniques. Our formulation is equipped with a base estimator that is coupled with a provably optimal balance heuristic and a practical stochastic MIS (SMIS) estimator that makes CMIS accessible to a broad range of problems. To illustrate the effectiveness and utility of our framework, we apply it to three different light transport applications, showing improved performance over the prior state-of-the-art techniques.

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  author = {Rex West and Iliyan Georgiev and Adrien Gruson and Toshiya Hachisuka},
  title = {Continuous Multiple Importance Sampling},
  journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH)},
  volume = {39},
  number = {4},
  article = {136},
  year = {2020},
  month = jul,
  doi = {10.1145/3386569.3392436},
  keywords = {multiple importance sampling, light transport, spectral rendering, path reuse, volume rendering}