System and method for training a neural network for visual localization based upon learning objects-of-interest dense match regression
US11003956B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 16, 2019 |
| Grant date | May 11, 2021 |
| Priority date | — |
| Expiry date | Sep 4, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30244
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for training, using a plurality of training images with corresponding six degrees of freedom camera pose for a given environment and a plurality of reference images, each reference image depicting an object-of-interest in the given environment and having a corresponding two-dimensional to three-dimensional correspondence for the given environment, a neural network to provide visual localization by: for each training image, detecting and segmenting object-of-interest in the training image; generating a set of two-dimensional to two-dimensional matches between the detected and segmented objects-of-interest and corresponding reference images; generating a set of two-dimensional to three-dimensional matches from the generated set of two-dimensional to two-dimensional matches and the two-dimensional to three-dimensional correspondences corresponding to the reference images; and determining localization, for each training image, by solving a perspective-n-point problem using the generated set of two-dimensional to three-dimensional matches.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.