OpenOF - Framework for Sparse Non-linear Least Squares Optimization on a GPU

Cornelius Wefelscheid, Olaf Hellwich

Abstract

In the area of computer vision and robotics non-linear optimization methods have become an important tool. For instance, all structure from motion approaches apply optimizations such as bundle adjustment (BA). Most often, the structure of the problem is sparse regarding the functional relations of parameters and measurements. The sparsity of the system has to be modeled within the optimization in order to achieve good performance. With OpenOF, a framework is presented, which enables developers to design sparse optimizations regarding parameters and measurements and utilize the parallel power of a GPU. We demonstrate the universality of our framework using BA as example. The performance and accuracy is compared to published implementations for synthetic and real world data.

References

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Paper Citation


in Harvard Style

Wefelscheid C. and Hellwich O. (2013). OpenOF - Framework for Sparse Non-linear Least Squares Optimization on a GPU . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 260-267. DOI: 10.5220/0004282702600267


in Bibtex Style

@conference{visapp13,
author={Cornelius Wefelscheid and Olaf Hellwich},
title={OpenOF - Framework for Sparse Non-linear Least Squares Optimization on a GPU},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={260-267},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004282702600267},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - OpenOF - Framework for Sparse Non-linear Least Squares Optimization on a GPU
SN - 978-989-8565-48-8
AU - Wefelscheid C.
AU - Hellwich O.
PY - 2013
SP - 260
EP - 267
DO - 10.5220/0004282702600267