Pose estimation of a single circle using default intrinsic calibration
This work addresses a specific challenge in computer vision for applications like augmented reality, but it is incremental as it builds on existing pose estimation methods for circular markers.
The paper tackles the problem of estimating the pose of a single circular marker with an uncalibrated camera, which typically requires multiple circles, by proposing a method to remove ambiguity and showing that approximate calibration can yield accurate results, with empirical validation.
Circular markers are planar markers which offer great performances for detection and pose estimation. For an uncalibrated camera with an unknown focal length, at least the images of at least two coplanar circles are generally required to recover their poses. Unfortunately, detecting more than one ellipse in the image must be tricky and time-consuming, especially regarding concentric circles. On the other hand, when the camera is calibrated, one circle suffices but the solution is twofold and can hardly be disambiguated. Our contribution is to put beyond this limit by dealing with the uncalibrated case of a camera seeing one circle and discussing how to remove the ambiguity. We propose a new problem formulation that enables to show how to detect geometric configurations in which the ambiguity can be removed. Furthermore, we introduce the notion of default camera intrinsics and show, using intensive empirical works, the surprising observation that very approximate calibration can lead to accurate circle pose estimation.