Type
ArticleAuthors
Singh, GurpritÖztireli, Cengiz
Ahmed, Abdalla G.M.
Coeurjolly, David
Subr, Kartic
Deussen, Oliver
Ostromoukhov, Victor
Ramamoorthi, Ravi
Jarosz, Wojciech
KAUST Department
KAUST, Saudi ArabiaDate
2019-06-07Online Publication Date
2019-06-07Print Publication Date
2019-05Embargo End Date
2020-05-01Permanent link to this record
http://hdl.handle.net/10754/656310
Metadata
Show full item recordAbstract
Modern physically based rendering techniques critically depend on approximating integrals of high dimensional functions representing radiant light energy. Monte Carlo based integrators are the choice for complex scenes and effects. These integrators work by sampling the integrand at sample point locations. The distribution of these sample points determines convergence rates and noise in the final renderings. The characteristics of such distributions can be uniquely represented in terms of correlations of sampling point locations. Hence, it is essential to study these correlations to understand and adapt sample distributions for low error in integral approximation. In this work, we aim at providing a comprehensive and accessible overview of the techniques developed over the last decades to analyze such correlations, relate them to error in integrators, and understand when and how to use existing sampling algorithms for effective rendering workflows.Citation
Singh, G., Öztireli, C., Ahmed, A. G. M., Coeurjolly, D., Subr, K., Deussen, O., … Jarosz, W. (2019). Analysis of Sample Correlations for Monte Carlo Rendering. Computer Graphics Forum, 38(2), 473–491. doi:10.1111/cgf.13653Sponsors
We are grateful to all the anonymous reviewers for their constructive remarks. This work was partially supported by the Fraunhofer and Max Planck cooperation program within the German pact for research and innovation (PFI). Kartic Subr was supported by a Royal Society University Research Fellowship, Ravi Ramamoorthi was supported by NSF grant 1451830 and Wojciech Jarosz was partially supported by NSF grant ISS-8127”96.Publisher
Blackwell Publishing LtdJournal
Computer Graphics ForumAdditional Links
https://people.mpi-inf.mpg.de/~gsingh/2019-singh-star.htmlhttp://diglib.eg.org/handle/10.1111/cgf13653
https://cs.dartmouth.edu/~wjarosz/publications/singh19analysis.html
ae974a485f413a2113503eed53cd6c53
10.1111/cgf.13653