## Coresets for Triangulation

- Qianggong Zhang, Tat-Jun Chin
- IEEE transactions on pattern analysis and machine…
- 2017

3 Excerpts

- Published 2015 in ICCV

Multi-camera triangulation of feature points based on a minimisation of the overall l2 reprojection error can get stuck in suboptimal local minima or require slow global optimisation. For this reason, researchers have proposed optimising the l∞ norm of the l2 single view reprojection errors, which avoids the problem of local minima entirely. In this paper we present a novel method for l∞ triangulation that minimizes the l∞ norm of the l∞ reprojection errors: this apparently small difference leads to a much faster but equally accurate solution which is related to the MLE under the assumption of uniform noise. The proposed method adopts a new optimisation strategy based on solving simple quadratic equations. This stands in contrast with the fastest existing methods, which solve a sequence of more complex auxiliary Linear Programming or Second Order Cone Problems. The proposed algorithm performs well: for triangulation, it achieves the same accuracy as existing techniques while executing faster and being straightforward to implement.

@inproceedings{Donn2015PointTT,
title={Point Triangulation through Polyhedron Collapse Using the l∞ Norm},
author={Simon Donn{\'e} and Bart Goossens and Wilfried Philips},
booktitle={ICCV},
year={2015}
}