@InProceedings{hajjami:cvprw:2020,
  author    = {Hajjami, Jaouad and Caracotte, Jordan and Caron, Guillaume and Napoleon, Thibault},
  title     = {ArUcOmni: detection of highly reliable fiducial markers in panoramic images},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2020},
  pages     = {2693--2699},
  address   = {Seattle, WA, USA},
  month     = {June 14-June 19},
  publisher = {IEEE},
  url       = {https://openaccess.thecvf.com/content\_CVPRW\_2020/papers/w38/Hajjami\_ArUcOmni\_Detection\_of\_Highly\_Reliable\_Fiducial\_Markers\_in\_Panoramic\_Images\_CVPRW\_2020\_paper.pdf},
  keywords  = {Cameras, Three-dimensional displays, Pose estimation, Sensors, Mathematical model, Reliability},
  doi       = {10.1109/CVPRW50498.2020.00325},
  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.}
}