On Filtering the Noise from the Random Parameters
in Monte Carlo Rendering


ACM Transactions on Graphics (TOG)
Vol. 31, No. 3, May 2012

     Pradeep Sen                     Soheil Darabi     

UNM Advanced Graphics Lab

Abstract

Monte Carlo (MC) rendering systems can produce spectacular images but are plagued with noise at low sampling rates. In this work, we observe that this noise occurs in regions of the image where the sample values are a direct function of the random parameters used in the Monte Carlo system. Therefore, we propose a way to identify MC noise by estimating this functional relationship from a small number of input samples. To do this, we treat the rendering system as a black box and calculate the statistical dependency between the outputs and inputs of the system. We then use this information to reduce the importance of the sample values affected by MC noise when applying an image-space, cross-bilateral filter, which removes only the noise caused by the random parameters but preserves important scene detail. The process of using the functional relationships between sample values and the random parameter inputs to filter MC noise is called random parameter filtering (RPF), and we demonstrate that it can produce images in a few minutes that are comparable to those rendered with a thousand times more samples. Furthermore, our algorithm is general because we do not assign any physical meaning to the random parameters, so it works for a wide range of Monte Carlo effects, including depth of field, area light sources, motion blur, and path-tracing. We present results for still images and animated sequences at low sampling rates that have higher quality than those produced with previous approaches.

The Video

On Filtering the Noise from the Random Parameters
in Monte Carlo Rendering

Paper and Additional Materials

Bibtex

@article{RPF_Sen2012,

author = {Sen, Pradeep and Darabi, Soheil},

title = {On Filtering the Noise from the Random Parameters in {M}onte {C}arlo Rendering},

journal = {ACM Trans. Graph.},

volume = {31},

number = {3},

articleno = {18},

pages = {18:1--18:15},

month = jun,

year = {2012},

}


Upon acceptance to TOG in May 2011, we published and widely disseminated a technical report which describes the implementation of the random parameter filtering (RPF) algorithm in detail. Although a copy of this technical report is available on this webpage for convenience, the copy of record can be found in LoboVault, UNM's institutional repository for scholarly publications, at this location. Authors referring to our RPF work might consider citing this technical report as well, as it predates the TOG paper by over a year:


@techreport{RPFTechReport,

author = {Pradeep Sen and Soheil Darabi},

title = {Implementation of {R}andom {P}arameter {F}iltering},

institution = {University of New Mexico},

number = {EECE-TR-11-0004},

month = {May},

year = {2011},

}



Commercialization

The information (source code, data sets, etc.) provided on this website is for non-commercial, research purposes only. For commercial inquiries regarding the algorithm presented in this paper, please contact Ms. Jovan Heusser in the UNM Technology Transfer Office at (505) 272-7908 or via email at jheusser@stc.unm.edu for more information.

This page contains documents, source code, videos and other files that could be protected by copyright. They are provided here for reasonable academic fair use. Interested parties are referred to the official published version of the documents which are available from the copyright holder through the external link.

Funding

This material is based upon work supported by the National Science Foundation under Grant No. 08-45396.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.