Abstract
Bundle adjustment is one of the main tools used for multiple view reconstruction. It seeks to refine the estimate of the 3D scene and the view parameter that minimize a certain cost function, e.g. the overall reprojection error. In this paper we propose a new algorithm to simplify the computation of the reprojection error for multiple views. With this algorithm, the bundle adjustment will be accelerated, whether the cameras are calibrated or un-calibrated. The proposed techniques for bundle adjustment are not only tolerant of missing data, but also allow the assignment of individual covariance to each image measurement. Experiments are conducted both on synthetic data and on real data to compare the proposed bundle adjustment techniques with the other existing methods. It is shown that the result obtained with the proposed techniques is comparable to that with the maximum likelihood estimation, however, it is more efficient.
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References
Bartoli, A.: A Unified Framework for Quasi-Linear Bundle Adjustment. In: Proc. of the 16th IAPR International Conference on Pattern Recognition, Quebec, Canada, vol. 2, August 2002, pp. 560–563 (2002)
Chen, Q., Medioni, G.: Efficient Iterative Solution to M-View Projective Reconstruction. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1065. Springer, Heidelberg (1996)
Hartley, R., Sturm, P.: Triangulation. Computer Vision and Image Understanding 68(2) (1997)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2001)
Kinatani, K.: Statistical Optimization for Geometric Computation: Theory and Practice. Elsevier, Amsterdam (1996)
Ma, Y., Vidal, R., Hsu, S., Sastry, S.: ptimal Motion Estimation from Multiple Images by Normalized Epipolar Constraint. Journal of Communications in Information and Systems (1), 51–73 (2001)
Mahamud, S., Herbert, M., Omori, Y., Ponce, J.: Provably-Convergent Iterative Methods for Projective Structure and Mothion. In: Proc. of CVPR (2001)
Triggs, B., McLauchlan, P., Hartley, R., Fitzgibbon, A.: Bundle Adjustment - A Modern Synthesis. In: Proc. of the International Workshop on Vision Algorithms: theory and Practice, Corfu, Greece, September 1999, pp. 864–884 (1999)
Vidal, R., Ma, Y., Hsu, S., Sastry, S.: Optimal Motion Estimation from Multiview Normalized Epipolar Constraint. In: Proc. of International Conference on Computer Vision, Vancouver, Canada, vol. 1, pp. 34–41 (2001)
Weng, J., Huang, T.S., Ahuja, N.: Optimal Motion and Structure Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(9), 864–884 (1993)
Zhang, Z., Shan, Y.: Incremental Motion Estimation Through Local Bundle Adjustment. Technical Report, MSR-TR-01-54 (2001)
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Liu, B., Yu, M., Maier, D., Männer, R. (2003). Accelerated Bundle Adjustment in Multiple-View Reconstruction. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_162
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DOI: https://doi.org/10.1007/978-3-540-45226-3_162
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40804-8
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