Invention Grant
- Patent Title: Neural network-based camera calibration
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Application No.: US16675641Application Date: 2019-11-06
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Publication No.: US10964060B2Publication Date: 2021-03-30
- Inventor: Kalyan K. Sunkavalli , Yannick Hold-Geoffroy , Sunil Hadap , Matthew David Fisher , Jonathan Eisenmann , Emiliano Gambaretto
- Applicant: ADOBE INC.
- Applicant Address: US CA San Jose
- Assignee: ADOBE INC.
- Current Assignee: ADOBE INC.
- Current Assignee Address: US CA San Jose
- Agency: Shook, Hardy & Bacon L.L.P.
- Main IPC: G06T7/80
- IPC: G06T7/80 ; G06N3/08 ; G06T7/00 ; G06N3/04

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.
Public/Granted literature
- US20200074682A1 NEURAL NETWORK-BASED CAMERA CALIBRATION Public/Granted day:2020-03-05
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