Issued Patents 2020
Showing 1–11 of 11 patents
| Patent # | Title | Co-Inventors | Date |
|---|---|---|---|
| 10818080 | Piecewise-polynomial coupling layers for warp-predicting neural networks | Thomas Müller, Brian McWilliams, Fabrice Rousselle | 2020-10-27 |
| 10796414 | Kernel-predicting convolutional neural networks for denoising | Thijs Vogels, Fabrice Rousselle, Brian McWilliams | 2020-10-06 |
| 10789686 | Denoising Monte Carlo renderings using machine learning with importance sampling | Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Mark Meyer | 2020-09-29 |
| 10706508 | Adaptive sampling in Monte Carlo renderings using error-predicting neural networks | Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Mark Meyer, Alex Harvill | 2020-07-07 |
| 10699382 | Denoising Monte Carlo renderings using neural networks with asymmetric loss | Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Mark Meyer, Alex Harvill | 2020-06-30 |
| 10672109 | Multi-scale architecture of denoising monte carlo renderings using neural networks | Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Mark Meyer, Alex Harvill | 2020-06-02 |
| 10607319 | Denoising monte carlo renderings using progressive neural networks | Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Mark Meyer | 2020-03-31 |
| 10586310 | Denoising Monte Carlo renderings using generative adversarial neural networks | Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Mark Meyer | 2020-03-10 |
| 10580194 | Informed choices in primary sample space for light transport simulation | Wenzel Jakob, Wojciech Jarosz, Benedikt Bitterli | 2020-03-03 |
| 10572979 | Denoising Monte Carlo renderings using machine learning with importance sampling | Thijs Vogels, Fabrice Rousselle, Brian McWilliams, Mark Meyer | 2020-02-25 |
| 10565685 | Denoising binned-depth images | David M. Adler, Delio Aleardo Vicini, Brent Burley, Fabrice Rousselle | 2020-02-18 |