Pareto meets huber: Efficiently avoiding poor minima in robust estimation
Paper in proceeding, 2019

Robust cost optimization is the task of fitting parameters to data points containing outliers. In particular, we focus on large-scale computer vision problems, such as bundle adjustment, where Non-Linear Least Square (NLLS) solvers are the current workhorse. In this context, NLLS-based state of the art algorithms have been designed either to quickly improve the target objective and find a local minimum close to the initial value of the parameters, or to have a strong ability to escape poor local minima. In this paper, we propose a novel algorithm relying on multi-objective optimization which allows to match those two properties. We experimentally demonstrate that our algorithm has an ability to escape poor local minima that is on par with the best performing algorithms with a faster decrease of the target objective.

epipolar geometry

structure from motion

Computer vision problems

Multiobjective optimization

3D reconstruction


Christopher Zach

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Guillaume Bourmaud

University of Bordeaux

Proceedings of the IEEE International Conference on Computer Vision

15505499 (ISSN)

Vol. 2019-October 10242-10250 9010256
978-172814803-8 (ISBN)

17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Seoul, South Korea,

Subject Categories

Control Engineering

Signal Processing

Computer Vision and Robotics (Autonomous Systems)



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