ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration
Abstract
We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV’s checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups.
Cite
Text
Placht et al. "ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10593-2_50Markdown
[Placht et al. "ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/placht2014eccv-rochade/) doi:10.1007/978-3-319-10593-2_50BibTeX
@inproceedings{placht2014eccv-rochade,
title = {{ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration}},
author = {Placht, Simon and Fürsattel, Peter and Mengue, Etienne Assoumou and Hofmann, Hannes G. and Schaller, Christian and Balda, Michael and Angelopoulou, Elli},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {766-779},
doi = {10.1007/978-3-319-10593-2_50},
url = {https://mlanthology.org/eccv/2014/placht2014eccv-rochade/}
}