Muhammad Arif
Department of Control Science and Engineering
Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
Huang Xinhan
Department of Control Science and Engineering
Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
Wang Min
Department of Control Science and Engineering
Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
ABSTRACT
3D reconstruction problem from images can be classified into three strata each of which is equivalent to the estimation of a specific geometry group. The simplest being projective, then affine, next metric and finally Euclidean structure. The advantage of stratification is that the images do not need to be from calibrated cameras in order to obtain reconstruction. In this paper results for both camera calibration and reconstruction are presented to verify that it is possible to obtain a 3D model directly from features in the images for man-made world.
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How to cite this article
Muhammad Arif, Huang Xinhan and Wang Min, 2002. Stratified Approach to 3D Reconstruction. Information Technology Journal, 1: 75-79.
DOI: 10.3923/itj.2002.75.79
URL: https://scialert.net/abstract/?doi=itj.2002.75.79
DOI: 10.3923/itj.2002.75.79
URL: https://scialert.net/abstract/?doi=itj.2002.75.79
REFERENCES
- Liebowitz, D. and A. Zisserman, 1999. Combining scene and auto-calibration constraints. Proc. 7th IEEE Int. Conf., 1: 293-300.
Direct Link - Longuet-Higgins, H.C., 1981. A computer algorithm for reconstructing a scene from two projections. Nature, 293: 133-135.
CrossRefDirect Link - Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8: 679-698.
CrossRefDirect Link - Dron, L., 1993. Dynamic camera self-calibration from controlled motion sequences. Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 15-17, 1993, New York, USA., pp: 501-506.
Direct Link - Pollefeys, M., L. van Gool and A. Oosterlinck, 1996. The modulus constraint: A new constraint self-calibration. Proc. Int. Conf. Patt. Recognition, 1: 349-353.
Direct Link - Pollefeys, M., R. Koch and L. Van-Gool, 1998. Self-calibration and metric reconstruction in spite of varying andunknown internal camera parameters. Proceedings of the 6th International Conference on Computer Vision, Jan. 4-7, Bombay, India, pp: 90-95.
Direct Link - Luong, Q.T. and O. Faugeras, 1996. The fundamental matrix: Theory, algorithms and stability analysis. Int. J. Comput. Vis., 17: 43-75.
Direct Link - Torr, P.H.S. and D.W. Murray, 1997. The development and comparison of robust methodsfor estimating the fundamental matrix. Int. J. Comput. Vis., 24: 271-300.
CrossRefDirect Link - Tsai, R.Y., 1987. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom., 3: 323-344.
Direct Link - Hartley, R.I., 1994. An algorithm for self calibration from several views. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 21-23, Seattle, WA, USA., pp: 908-912.
Direct Link - Maybank, S.J. and O.D. Faugeras, 1992. A theory of self-calibration of a moving camera. Int. J. Comput. Vision, 8: 123-151.
Direct Link - Zhang, Z., 1998. Determining the epipolar geometry and its uncertainty: A Review. Int. J. Comput. Vis., 27: 161-195.
CrossRefDirect Link - Luong, Q.T. and O.D. Faugeras, 1996. Canonical representations of the geometeries of multiple projective views. Comput. Vis. Image Understanding, 64: 193-229.
Direct Link