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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors optical sensors
- humanities arts modern and contemporary art cinematography
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2015
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
08002 Barcelona
Spain
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.