Abstract
Robust estimation is used in a wide range of applications. One of the most popular algorithms for robust estimation is the random sample consensus (ransac) that achieves a high degree of accuracy even with a significant amount of outliers. A major drawback of ransac is the fast increasing number of iterations caused by higher outlier ratios, resulting in increasing computational costs. In this paper FestGPU, a framework for Fast robust ESTimation on GPU, is presented which reaches a speedup of up to 135\(\times\) compared to a singlecore CPU. Together with a C++ and a Matlab interface the framework is made publicly available on the authors’ website for the research community.
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Acknowledgments
This work was developed in the project AVIGLE funded by the State of North Rhine Westphalia (NRW), Germany, and the European Union, European Regional Development Fund “Europe—Investing in your future“. AVIGLE was conducted in cooperation with several industrial and academic partners. We thank all the project partners for their work and contributions to the project. Furthermore, we thank Cenalo GmbH for the image acquisition.
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Roters, J., Jiang, X. FestGPU: a framework for fast robust estimation on GPU. J Real-Time Image Proc 13, 759–772 (2017). https://doi.org/10.1007/s11554-014-0439-5
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DOI: https://doi.org/10.1007/s11554-014-0439-5