Browse > Article
http://dx.doi.org/10.6109/jkiice.2017.21.10.1923

Recursive Error-Component Correcting Method for 3D Shape Reconstruction  

Koh, Sung-shik (Mobile Comm., Samsung Electronics Co., Ltd.)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.10, 2017 , pp. 1923-1928 More about this Journal
Abstract
This paper is a study on error correction for three-dimensional shape reconstruction based on factorization method. The existing error correction method based on factorization has a limitation of correction because it is optimized globally. Thus in this paper, we propose our new method which can find and correct the only major error influence factor toward three-dimensional reconstructed shape instead of global approach. We define the error-influenced factor in two-dimensional re-projection deviation space and directly control the error components. In addition, it is possible to improve the error correcting performance by recursively applying the above process. This approach has an advantage under noise because it controls the major error components without depending on any geometric information. The performance evaluation of the proposed algorithm is verified by simulation with synthetic and real image sequence to demonstrate noise robustness.
Keywords
Three-dimensional reconstruction; SVD; Factorization; Recursive error correction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Martinello, A. Wajs, S. Quan, H. Lee, C. Lim, T. Woo, W. Lee, S. S. Kim, and D. Lee, "Dual Aperture Photography: Image and Depth from a Mobile Camera Computational Photography" in Proceedings of the 2015 IEEE International Conference on Computational Photograph, Houston: TX, pp. 1-10, 2015.
2 G. S. Lee, Y. W. Choi, M. H. Lee, S. G. Kim, and G. S. Cho, "Reconnaissance Surveying for Cultural Assets using Unmanned Aerial Vehicle," Journal of the Korean Cadastre Information Association, vol. 18, no. 3, pp. 25-34, Dec. 2016.
3 D. W. Shin and Y. S. Ho, "Implementation of 3D object reconstruction using a pair of kinect cameras," in Proceeding of the 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Chian Mai: Thailand, pp. 1-4, 2014.
4 F. Santoso, M. A. Garratt, M. R. Pickering, and M. Asikuzzaman, "3D Mapping for Visualization of Rigid Structures: A Review and Comparative Study," International Journal of IEEE Sensors, vol. 16, no. 6, pp. 1484-1507, Mar. 2016.   DOI
5 Q. T. Luong and O. Faugeras, "The fundamental matrix: Theory, algorithms, and stability analysis," International Journal of Computer Vision, vol. 17, no. 1, pp. 43-76, Jan. 1996.   DOI
6 C. Tomasi and T. Kanade, "Shape and motion from image streams under orthography: A factorization method," International Journal of Computer Vision, vol. 9, no. 2, pp. 137-154, Nov. 1992.   DOI
7 B. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon, "Bundle adjustment - A modern synthesis," in Vision Algorithms: Theory and Practice (W. Triggs, A. Zisserman, and R. Szeliski, eds.), New York: Springer Verlag, pp. 298-375, 2000.
8 Y. S. Hung, W. K. Tang, "Projective reconstruction from multiple views with minimization of 2D reprojection error," International Journal of Computer Vision, vol.66, no. 3, pp. 305-317, Mar. 2006.   DOI
9 S. Baker and I. Matthews, "Lucas-Kanade 20 Years On: A Unifying Framework," International Journal of Computer Vision, vol.56, no. 3, pp. 221-255, Mar. 2004.   DOI