Learning to estimate high-dynamic range outdoor lighting parameters
US10936909B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 12, 2018 |
| Grant date | Mar 2, 2021 |
| Priority date | — |
| Expiry date | Jul 3, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20208
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems are provided for determining high-dynamic range lighting parameters for input low-dynamic range images. A neural network system can be trained to estimate lighting parameters for input images where the input images are synthetic and real low-dynamic range images. Such a neural network system can be trained using differences between a simple scene rendered using the estimated lighting parameters and the same simple scene rendered using known ground-truth lighting parameters. Such a neural network system can also be trained such that the synthetic and real low-dynamic range images are mapped in roughly the same distribution. Such a trained neural network system can be used to input a low-dynamic range image determine high-dynamic range lighting parameters.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.