Neural network-based camera calibration
US10515460B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 29, 2017 |
| Grant date | Dec 24, 2019 |
| Priority date | — |
| Expiry date | Dec 2, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20081
- WIPO fieldAudio-visual technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention provide systems, methods, and computer storage media directed to generating training image data for a convolutional neural network, encoding parameters into a convolutional neural network, and employing a convolutional neural network that estimates camera calibration parameters of a camera responsible for capturing a given digital image. A plurality of different digital images can be extracted from a single panoramic image given a range of camera calibration parameters that correspond to a determined range of plausible camera calibration parameters. With each digital image in the plurality of extracted different digital images having a corresponding set of known camera calibration parameters, the digital images can be provided to the convolutional neural network to establish high-confidence correlations between detectable characteristics of a digital image and its corresponding set of camera calibration parameters. Once trained, the convolutional neural network can receive a new digital image, and based on detected image characteristics thereof, estimate a corresponding set of camera calibration parameters with a calculated level of confidence.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.