Abstract
A novel linear algorithm to estimate the camera pose from known correspondences of 3D points and their 2D image points is proposed based on the angle constraints from arbitrary three points in 3D point set. Compared with Ansar’s N Point Linear method which is based on the distance constraints between 3D points, due to more strict geometric constraints, this approach is more accurate. Simultaneously some strategies of choosing constraint equations are introduced so that this algorithm’s computational complexity is reduced. In order to obtain more accurate estimated pose, we propose the singular value decomposition method to derive the parameters from their quadratic terms more exactly. Finally, the experiments show our approach’s effectiveness and accuracy compared with the other two algorithms using synthetic data and real images.
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References
Ansar, A., Daniilidis, K.: Linear pose estimation from points or lines. IEEE Trans. Pattern Analysis Machine Intell. 25(5), 578–589 (2003)
Kipnis, A., Shamir, A.: Cryptanalysis of the HFE public key cryptosystem by relinearization. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 19–30. Springer, Heidelberg (1999)
Quan, L., Lan, Z.D.: Linear N-point camera pose determination. IEEE Trans. Pattern Analysis Machine Intell. 21(8), 774–780 (1999)
Moreno-Noguer, F., Lepetit, V., Fua, P.: Accurate non-iterative on solution to the PnP problem. In: Proc. IEEE Conf. on Computer Vision (2007)
Fiore, P.D.: Efficient linear solution of exterior orientation. IEEE Trans. Pattern Analysis Machine Intell. 23(2), 140–148 (2001)
Lowe, D.G.: Fitting parameterized three-Dimensional models to images. IEEE Trans. Pattern Analysis Machine Intell. 13(5), 441–450 (1991)
Haralick, R.M.: Pose estimation from corresponding point data. IEEE Trans. Systems, Man, and Cybernetics 19(6), 1426–1446 (1989)
Liu, Y., Huang, T.S., Faugeras, O.D.: Determination of camera location from 2-d to 3-d line and point correspondences. IEEE Trans. Pattern Analysis Machine Intell. 12(1), 28–37 (1990)
Hartley, R.I.: Minimizing algebraic error in geometric estimation problems. In: Proc. IEEE Conf. on Computer Vision, pp. 469–476 (1998)
Umeyama, S.: Least-Squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Analysis Machine Intell. 13(4), 376–380 (1991)
Heikkilä, J.: Geometric camera calibration using circular control points. IEEE Trans. Pattern Analysis Machine Intell. 22(10), 1066–1077 (2000)
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Wang, F., Jiang, C., Zheng, N., Guo, Y. (2010). Camera Pose Estimation Based on Angle Constraints. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_10
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DOI: https://doi.org/10.1007/978-3-642-17289-2_10
Publisher Name: Springer, Berlin, Heidelberg
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