Temporal techniques of denoising Monte Carlo renderings using neural networks
US11532073B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jul 31, 2018 |
| Grant date | Dec 20, 2022 |
| Priority date | — |
| Expiry date | Oct 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20182
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A modular architecture is provided for denoising Monte Carlo renderings using neural networks. The temporal approach extracts and combines feature representations from neighboring frames rather than building a temporal context using recurrent connections. A multiscale architecture includes separate single-frame or temporal denoising modules for individual scales, and one or more scale compositor neural networks configured to adaptively blend individual scales. An error-predicting module is configured to produce adaptive sampling maps for a renderer to achieve more uniform residual noise distribution. An asymmetric loss function may be used for training the neural networks, which can provide control over the variance-bias trade-off during denoising.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.