
TL;DR
This paper introduces a camera calibration system called Calibration with Pose Guidance that enhances accuracy and consistency, reducing user dependency and streamlining calibration for large-scale applications.
Contribution
The paper presents a novel pose-guided calibration method that improves accuracy and reduces variance compared to traditional plane pattern-based calibration tools.
Findings
Achieves more accurate calibration results.
Reduces calibration variance among users.
Streamlines calibration for large-scale deployments.
Abstract
Camera calibration plays a critical role in various computer vision tasks such as autonomous driving or augmented reality. Widely used camera calibration tools utilize plane pattern based methodology, such as using a chessboard or AprilTag board, user's calibration expertise level significantly affects calibration accuracy and consistency when without clear instruction. Furthermore, calibration is a recurring task that has to be performed each time the camera is changed or moved. It's also a great burden to calibrate huge amounts of cameras such as Driver Monitoring System (DMS) cameras in a production line with millions of vehicles. To resolve above issues, we propose a calibration system called Calibration with Pose Guidance to improve calibration accuracy, reduce calibration variance among different users or different trials of the same person. Experiment result shows that our…
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