Study on Camera Pose Estimation

Xiao Zhou Zhu; Xiao Qian Chen; Xin Song

doi:10.4028/www.scientific.net/AMM.631-632.462

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 631-632Study on Camera Pose Estimation

Study on Camera Pose Estimation

Abstract:

A common need in photogrammetry, robotics and computer vision is performing camera pose estimation. A comparative analysis is presented here for three classical and representative algorithms, including direct linear transform (DLT), EPNP and Cayley method, each of which computes the translation and rotation matrix using non-iterative method with six or more point correspondences. The comparison shows qualitative and quantitative experimental results to determine (1) the accuracy and robustness under the influence of different levels of noise, (2) the accuracy, robustness and efficiency for different sizes of point correspondences.

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Periodical:

Applied Mechanics and Materials (Volumes 631-632)

Pages:

462-469

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.631-632.462

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Online since:

September 2014

Authors:

Xiao Zhou Zhu, Xiao Qian Chen*, Xin Song

Keywords:

Camera Pose Estimation, Cayley Transformation, Direct Linear Transform (DLT), EPNP

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* - Corresponding Author

References

[1] B.K.P. Horn, H.M. Hilden, and S. Negahdaripour. Closed Form Solution of Absolute Orientation Using Unit Quaternions, Journal of the Optical Society America, vol. 4, no. 4, pp.629-642, (1987).

DOI: 10.1364/josaa.4.000629

Google Scholar

[2] K. S. Arun, T. S. Huang, and S. D. Blostein. Least-Squares Fitting of Two 3-D Point Sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 5, pp.698-700, (1987).

DOI: 10.1109/tpami.1987.4767965

Google Scholar

[3] S. Umeyama. Least-Squares Estimation of Transformation Parameters between Two Point Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 4, pp.376-380, (1991).

DOI: 10.1109/34.88573

Google Scholar

[4] M. A. Fischler, and R. C. Bolles. Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Commun. ACM, vol. 24, no. 6, pp.381-395, (1981).

DOI: 10.1145/358669.358692

Google Scholar

[5] Y. Abdel-Aziz, and H. Karara. Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-range Photogrammetry, in ASP/UI Symp. Close-Range Photogrammetry, (1971).

DOI: 10.14358/pers.81.2.103

Google Scholar

[6] F. Moreno-Noguer, V. Lepetit, and P. Fua. Accurate Non-Iterative O(n) Solution to the PnP Problem, in Proceedings of 11th IEEE International Conference on Computer Vision, (2007).

DOI: 10.1109/iccv.2007.4409116

Google Scholar

[7] F. C. Wu. Computer Vision-Cayley Transformation and Metric Reconstruction, Beijing, China: Science Express, (2011).

Google Scholar

[8] F. C. Wu, M. Zhang, and Z. Y. Hu. Self-Calibration Under the Cayley Framework, International Journal of Computer Vision, vol. 103, no. 3, pp.372-398, (2013).

DOI: 10.1007/s11263-013-0610-7

Google Scholar