Abstract
We present a highly efficient method for estimating the structure and motion from image sequences taken by uncalibrated cameras. The basic principle is to do projective reconstruction first followed by Euclidean upgrading. However, the projective reconstruction step dominates the total computational time, because we need to solve eigenproblems of matrices whose size depends on the number of frames or feature points. In this paper, we present a new algorithm that yields the same solution using only matrices of constant size irrespective of the number of frames or points. We demonstrate the superior performance of our algorithm, using synthetic and real video images.
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References
Ackermann, H., Kanatani, K.: Robust and efficient 3-D reconstruction by self-calibration. In: Proc. IAPR Conf. Machine Vision Applications, Tokyo, Japan, May 2007, pp. 178–181 (2007)
Golub, T.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns-Hopkins University Press, Baltimore, MD, U.S.A. (1996)
Hartley, R.: In defense of the 8-point algorithm. IEEE Trans. Patt. Anal. Mach. Intell. 19(6), 580–593 (1997)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)
Heyden, A., Berthilsson, R., Sparr, G.: An iterative factorization method for projective structure and motion from image sequences. Image Vis. Comput. 17(13), 981–991 (1999)
Kanatani, K.: Latest progress of 3-D reconstruction from multiple camera images. In: Guo, X.P. (ed.) Robotics Research Trends, Nova Science, Hauppauge, NY, U.S.A., pp. 33–75 (2008)
Mahamud, S., Hebert, M.: Iterative projective reconstruction from multiple views. In: Proc. IEEE Conf. Comput. Vis. Patt. Recog., Hinton Head Island, SC, U.S.A., June 2000, vol. 2, pp. 2430–2437 (2000)
Pollefeys, M., Koch, R., Van Gool, L.: Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. Int. J. Comput. Vis. 32(1), 7–25 (1999)
Sturm, P., Triggs, B.: A factorization based algorithm for multi-image projective structure and motion. In: Proc. 4th Euro. Conf. Comput. Vis., Cambridge, U.K., April 1996, vol. 2, pp. 709–720 (1996)
Tomasi, C., Kanade, T.: Detection and Tracking of Point Features. CMU Tech. Rept. CMU-CS-91132 (1991), http://vision.stanford.edu/~birch/klt/
Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: A factorization method. Int. J. Comput. Vis. 9(2), 137–154 (1992)
Triggs, B., et al.: Bundle adjustment—A modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) Vision Algorithms: Theory and Practice, pp. 298–375. Springer, Berlin (2000)
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Ackermann, H., Kanatani, K. (2008). Iterative Low Complexity Factorization for Projective Reconstruction. In: Sommer, G., Klette, R. (eds) Robot Vision. RobVis 2008. Lecture Notes in Computer Science, vol 4931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78157-8_12
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DOI: https://doi.org/10.1007/978-3-540-78157-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78156-1
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