A Spherical Eye from Multiple Cameras (Makes Better Models of the World)
Abstract
The paper describes an imaging system that has been designed specifically for the purpose of recovering egomotion and structure from video. The system consists of six cameras in a network arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system's own motion and consequently the estimation of shape models from the individual cameras. The reason is that inherent ambiguities of confusion between translation and rotation disappear. We provide algorithms for the calibration of the system and 3D motion estimation. The calibration is based on a new geometric constraint that relates the images of lines parallel in space to the rotation between the cameras. The 3D motion estimation uses a constraint relating structure directly to image gradients.
Cite
Text
Baker et al. "A Spherical Eye from Multiple Cameras (Makes Better Models of the World)." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990525Markdown
[Baker et al. "A Spherical Eye from Multiple Cameras (Makes Better Models of the World)." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/baker2001cvpr-spherical/) doi:10.1109/CVPR.2001.990525BibTeX
@inproceedings{baker2001cvpr-spherical,
title = {{A Spherical Eye from Multiple Cameras (Makes Better Models of the World)}},
author = {Baker, Patrick and Fermüller, Cornelia and Aloimonos, Yiannis and Pless, Robert},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {I:576-583},
doi = {10.1109/CVPR.2001.990525},
url = {https://mlanthology.org/cvpr/2001/baker2001cvpr-spherical/}
}