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

Cornelius Wefelscheid, Olaf Hellwich
Computer Vision and Remote Sensing, Berlin University of Technology, Sekr. MAR 6-5, Marchstrasse 23, D-10587, Berlin, Germany
9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 2013
@article{wefelscheid2013framework,

   title={Framework for Sparse Non-linear Least Squares Optimization on a GPU},

   author={Wefelscheid, Cornelius and Hellwich, Olaf},

   year={2013}

}

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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.
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