Generation of synthetic images for training a neural network model
US11182649B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 11, 2020 |
| Grant date | Nov 23, 2021 |
| Priority date | — |
| Expiry date | Dec 11, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/07
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Training deep neural networks requires a large amount of labeled training data. Conventionally, labeled training data is generated by gathering real images that are manually labelled which is very time-consuming. Instead of manually labelling a training dataset, domain randomization technique is used generate training data that is automatically labeled. The generated training data may be used to train neural networks for object detection and segmentation (labelling) tasks. In an embodiment, the generated training data includes synthetic input images generated by rendering three-dimensional (3D) objects of interest in a 3D scene. In an embodiment, the generated training data includes synthetic input images generated by rendering 3D objects of interest on a 2D background image. The 3D objects of interest are objects that a neural network is trained to detect and/or label.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.