calibDB: enabling web based computer vision through on-the-fly camera calibration

Abstract

Fraunhofer IGD (a) Calibrated camera matrix only (b) Rectified image (c) Fully calibrated camera Figure 1: Effect of camera calibration on an augmented reality scene: Although a calibrated camera matrix is used in (a), the misalignment is clearly visible. Using a complete distortion model allows rectifying the image (b). Together with an adapted camera matrix, this results in a fully aligned augmentation (c).ABSTRACT For many computer vision applications, the availability of camera calibration data is crucial as overall quality heavily depends on it.While calibration data is available on some devices through Augmented Reality (AR) frameworks like ARCore and ARKit, for most cameras this information is not available. Therefore, we propose a web based calibration service that not only aggregates calibration data, but also allows calibrating new cameras on-the-fly. We build upon a novel camera calibration framework that enables even novice users to perform a precise camera calibration in about 2 minutes. This allows general deployment of computer vision algorithms on the web, which was previously not possible due to lack of calibration data.

Which Way Is ‘Yildiz Amfi̇’? Augmented Reality vs. Paper Map on Pedestrian Wayfinding

“…By compensating a registration error, it would be possible to stabilize the display even if the AR platform is under motion. In order to avoid the occurrence of these errors, a calibration process is often required (Rojtberg and Gorschlüter, 2019).…”