Machine learning based model localization system
US10977818B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 23, 2018 |
| Grant date | Apr 13, 2021 |
| Priority date | — |
| Expiry date | Apr 21, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30244
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for deriving an image sensor's 3D pose estimate from a 2D scene image input includes at least one Machine Learning algorithm trained a priori to generate a 3D depth map estimate from the 2D image input, which is used in conjunction with physical attributes of the source imaging device to make an accurate estimate of the imaging device 3D location and orientation relative to the 3D content of the imaged scene. The system may optionally employ additional Machine Learning algorithms to recognize objects within the scene to further infer contextual information about the scene, such as the image sensor pose estimate relative to the floor plane or the gravity vector. The resultant refined imaging device localization data can be applied to static (picture) or dynamic (video), 2D or 3D images, and is useful in many applications, most specifically for the purposes of improving the realism and accuracy of primarily static, but also dynamic Augmented Reality (AR) applications.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.