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BMC Rendering

Bayesian Monte Carlo for Global Illumination

Total Cost €


EC-Contrib. €






Project "BMC Rendering" data sheet

The following table provides information about the project.


Organization address
address: PLACA DE LA MERCE, 10-12
postcode: 8002

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Project website
 Total cost 158˙121 €
 EC max contribution 158˙121 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-08-01   to  2018-07-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSIDAD POMPEU FABRA ES (BARCELONA) coordinator 158˙121.00


 Project objective

One of the most challenging problems in computer graphics (CG) is to synthesize physically-based realistic images given an accurate model of a virtual scene. Rendering a photo-realistic image requires solving the illumination integral which describes the light transport on a scene, and whose value is in general computed by resorting to numerical approximations such as those based on Monte Carlo (MC) methods.

The quality of the approximation of those methods is strongly dependent on the samples placement and weighting. Therefore several works have focused on improving the purely random sampling used in classic MC techniques. In particular, a recent and innovative approach called Bayesian Monte Carlo (BMC) has been proven to greatly outperform classic MC methods due to its ability to incorporate prior knowledge which is then used for careful samples weighting and placement. This method was successfully applied in rendering by Brouillat et al. (2009) but only for diffuse materials. Recently, Marques et al. (2013) have generalized the application of BMC to non-diffuse materials.

These works have confirmed the potential of BMC for efficiently solving the rendering integral, making it a new trend in computer graphics. Nevertheless, the use of BMC in CG is still in an incipient phase and its application to more evolved and widely used rendering algorithms remains cumbersome.

We propose a research plan with a double objective: first, to develop an adaptive sampling strategy for BMC integration, where a new set of samples is used to further improve the approximation of the previous integral estimate. Second, to apply BMC to higher dimension problems such as path tracing, where the integration over all possible ray paths turns the approximation into a high-dimension integration problem, hence addressing the holy grail of the integration in light transport simulation: the curse of dimensionality.


year authors and title journal last update
List of publications.
2019 Ricardo Marques, Christian Bouville, Kadi Bouatouch, Josep Blat
Extensible Spherical Fibonacci Grids
published pages: 1-1, ISSN: 1077-2626, DOI: 10.1109/tvcg.2019.2952131
IEEE Transactions on Visualization and Computer Graphics 2019-11-26
2018 Ricardo Marques, Christian Bouville, Kadi Bouatouch
Optimal Sample Weights for Hemispherical Integral Quadratures
published pages: , ISSN: 0167-7055, DOI: 10.1111/cgf.13392
Computer Graphics Forum 2019-06-13
2019 Ricardo Marques, Christian Bouville, Kadi Bouatouch
Spectral Analysis of Quadrature Rules and Fourier Truncation-based Methods Applied to Shading Integrals
published pages: 1-1, ISSN: 1077-2626, DOI: 10.1109/tvcg.2019.2913418
IEEE Transactions on Visualization and Computer Graphics 2019-06-06

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