Dynamically estimating light-source-specific parameters for digital images using a neural network
US11538216B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 3, 2019 |
| Grant date | Dec 27, 2022 |
| Priority date | — |
| Expiry date | Sep 3, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2219/2012
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to methods, non-transitory computer readable media, and systems that can render a virtual object in a digital image by using a source-specific-lighting-estimation-neural network to generate three-dimensional (“3D”) lighting parameters specific to a light source illuminating the digital image. To generate such source-specific-lighting parameters, for instance, the disclosed systems utilize a compact source-specific-lighting-estimation-neural network comprising both common network layers and network layers specific to different lighting parameters. In some embodiments, the disclosed systems further train such a source-specific-lighting-estimation-neural network to accurately estimate spatially varying lighting in a digital image based on comparisons of predicted environment maps from a differentiable-projection layer with ground-truth-environment maps.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.