Robust Fitting for Multiple View Geometry
Abstract
How hard are geometric vision problems with outliers? We show that for most fitting problems, a solution that minimizes the number of outliers can be found with an algorithm that has polynomial time-complexity in the number of points (independent of the rate of outliers). Further, and perhaps more interestingly, other cost functions such as the truncated L _2-norm can also be handled within the same framework with the same time complexity. We apply our framework to triangulation, relative pose problems and stitching, and give several other examples that fulfill the required conditions. Based on efficient polynomial equation solvers, it is experimentally demonstrated that these problems can be solved reliably, in particular for low-dimensional models. Comparisons to standard random sampling solvers are also given.
Cite
Text
Enqvist et al. "Robust Fitting for Multiple View Geometry." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33718-5_53Markdown
[Enqvist et al. "Robust Fitting for Multiple View Geometry." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/enqvist2012eccv-robust/) doi:10.1007/978-3-642-33718-5_53BibTeX
@inproceedings{enqvist2012eccv-robust,
title = {{Robust Fitting for Multiple View Geometry}},
author = {Enqvist, Olof and Ask, Erik and Kahl, Fredrik and Åström, Kalle},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {738-751},
doi = {10.1007/978-3-642-33718-5_53},
url = {https://mlanthology.org/eccv/2012/enqvist2012eccv-robust/}
}