Summary
The use of Computer Graphics (CG) is constantly expanding, e.g., in Virtual and Augmented Reality, requiring realistic interactive renderings of complex virtual environments at a much wider scale than available today. CG has many limitations we must overcome to satisfy these demands. High-quality accurate rendering needs expensive simulation, while fast approximate rendering algorithms have no guarantee on accuracy; both need manually-designed expensive-to-create content. Capture (e.g., reconstruction from photos) can provide content, but it is uncertain (i.e., inaccurate and incomplete). Image-based rendering (IBR) can display such content, but lacks flexibility to modify the scene. These different rendering algorithms have incompatible but complementary tradeoffs in quality, speed and flexibility; they cannot currently be used together, and only IBR can directly use captured content. To address these problems
FunGraph will revisit the foundations of Computer Graphics, so these disparate methods can be used together, introducing the treatment of uncertainty to achieve this goal.
FunGraph introduces estimation of rendering uncertainty, quantifying the expected error of rendering components, and propagation of input uncertainty of captured content to the renderer. The ultimate goal is to define a unified renderer exploiting the advantages of each approach in a single algorithm. Our methodology builds on the use of extensive synthetic (and captured) “ground truth” data, the domain of Uncertainty Quantification adapted to our problems and recent advances in machine learning – Bayesian Deep Learning in particular.
FunGraph will fundamentally transform computer graphics, and rendering in particular, by proposing a principled methodology based on uncertainty to develop a new generation of algorithms that fully exploit the spectacular (but previously incompatible) advances in rendering, and fully benefit from the wealth offered by constantly improving captured content.
FunGraph will revisit the foundations of Computer Graphics, so these disparate methods can be used together, introducing the treatment of uncertainty to achieve this goal.
FunGraph introduces estimation of rendering uncertainty, quantifying the expected error of rendering components, and propagation of input uncertainty of captured content to the renderer. The ultimate goal is to define a unified renderer exploiting the advantages of each approach in a single algorithm. Our methodology builds on the use of extensive synthetic (and captured) “ground truth” data, the domain of Uncertainty Quantification adapted to our problems and recent advances in machine learning – Bayesian Deep Learning in particular.
FunGraph will fundamentally transform computer graphics, and rendering in particular, by proposing a principled methodology based on uncertainty to develop a new generation of algorithms that fully exploit the spectacular (but previously incompatible) advances in rendering, and fully benefit from the wealth offered by constantly improving captured content.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/788065 |
| Start date: | 01-10-2018 |
| End date: | 30-09-2024 |
| Total budget - Public funding: | 2 497 161,00 Euro - 2 497 161,00 Euro |
Cordis data
Original description
The use of Computer Graphics (CG) is constantly expanding, e.g., in Virtual and Augmented Reality, requiring realistic interactive renderings of complex virtual environments at a much wider scale than available today. CG has many limitations we must overcome to satisfy these demands. High-quality accurate rendering needs expensive simulation, while fast approximate rendering algorithms have no guarantee on accuracy; both need manually-designed expensive-to-create content. Capture (e.g., reconstruction from photos) can provide content, but it is uncertain (i.e., inaccurate and incomplete). Image-based rendering (IBR) can display such content, but lacks flexibility to modify the scene. These different rendering algorithms have incompatible but complementary tradeoffs in quality, speed and flexibility; they cannot currently be used together, and only IBR can directly use captured content. To address these problemsFunGraph will revisit the foundations of Computer Graphics, so these disparate methods can be used together, introducing the treatment of uncertainty to achieve this goal.
FunGraph introduces estimation of rendering uncertainty, quantifying the expected error of rendering components, and propagation of input uncertainty of captured content to the renderer. The ultimate goal is to define a unified renderer exploiting the advantages of each approach in a single algorithm. Our methodology builds on the use of extensive synthetic (and captured) “ground truth” data, the domain of Uncertainty Quantification adapted to our problems and recent advances in machine learning – Bayesian Deep Learning in particular.
FunGraph will fundamentally transform computer graphics, and rendering in particular, by proposing a principled methodology based on uncertainty to develop a new generation of algorithms that fully exploit the spectacular (but previously incompatible) advances in rendering, and fully benefit from the wealth offered by constantly improving captured content.
Status
SIGNEDCall topic
ERC-2017-ADGUpdate Date
27-04-2024
Images
No images available.
Geographical location(s)
