ArUcOmni: Detection of Highly Reliable Fiducial Markers in Panoramic Images

Jaouad Hajjami, Jordan Caracotte, Guillaume Caron, Thibault Napoleon; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 634-635

Abstract


In this paper, we propose an adaptation of marker detection algorithm for panoramic cameras such as catadioptric and fisheye sensors. Due to distortions and non-uniform resolution of such sensors, the methods that are commonly used in perspective images cannot be applied directly. This work is in contrast with the existing marker detection framework: Automatic reliable fiducial markers Under occlusion (ArUco) for a conventional camera. To keep the same performance for panoramic cameras, our method is based on a spherical representation of the image that allows the marker to be detected and to estimate its 3D pose. We evaluate our approach on a new shared dataset that consists of a 3D rig of markers taken with two different sensors: a catadioptric camera and a fisheye camera. The evaluation has been performed against ArUco algorithm without rectification and with one of the rectified approaches based on the fisheye model.

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[bibtex]
@InProceedings{Hajjami_2020_CVPR_Workshops,
author = {Hajjami, Jaouad and Caracotte, Jordan and Caron, Guillaume and Napoleon, Thibault},
title = {ArUcOmni: Detection of Highly Reliable Fiducial Markers in Panoramic Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2020}
}