Adaptive sampling in Monte Carlo renderings using error-predicting neural networks
US10706508B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jul 31, 2018 |
| Grant date | Jul 7, 2020 |
| Priority date | — |
| Expiry date | Dec 26, 2038 |
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.