Abstract
The development of image preprocessing has provided new opportunities in the field of three-dimensional reconstruction. One of the most important areas of three-dimensional reconstruction is focused on model registration by means of matching algorithm. This is mainly due to the great increase of registration algorithm in the pattern recognition system such as image acquisition, image preprocessing, 3D reconstruction. This paper presents an analysis of model registration algorithm of three-dimensional reconstruction by comparison common registration algorithm such as RANSAC (Random Sample Consensus) and ICP (Iterative Closest Point). Then, in order to elevate registration precision and robustness affecting the 3D reconstruction results, CTF (Coarse to Fine) registration strategy based on RANSAC-ICP Algorithm is proposed. Finally, by using three-dimensional reconstruction experiment based on RANSAC-ICP Algorithm, the performance of CTF registration strategy has been analyzed, and some problems and design solutions have been identified and registration precision and robustness have also been validated by experimental results.
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References
Yoon, K., Kweon, I.: Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell. 4(28), 650–656 (2008)
Huang, X.: Cooperative optimization for energy minimization in computer: a case study of stereomatching. Technical report MSRTR-98-71, Microsoft Research, January 2007
Li, G., Zucker, S.W.: Surface geometric constraints for stereo in belief propagation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, no. 5, pp. 2355–2362 (2006)
Hosni, A., Bleyer, M., Gelautz, M.: Local stereo matching using geodesic support weights. In: International Conference on Image Processing, pp. 245–252 (2009)
Yoon, K., Kweon, I.: Support aggregation via non-linear diffusion with disparity dependent support-weight for stereo matching. In: Asian Conference on Computer Vision, pp. 1000–1003 (2009)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)
Chen, C.S., Hung, Y.P., Cheng, J.B.: RANSAC-based DARCES: a new approach to fast automatic registration of partially overlapping range images. IEEE Trans. PAMI 21(11), 1229–1234 (1999)
Besl, P.J., McKay, N.D.: A method for registration of shapes. Trans. PAMI 14(2), 239–245 (1992)
Boutteau, R., Savatier, X., Ertaud, J.Y.: A dynamic programming algorithm applied to omnidirectional vision for dense 3D reconstruction. In: Pattern Recognition (ACPR), pp. 927–931 (2013)
Mesko, M., Krsak, E.: Fast segment iterative algorithm for 3D reconstruction. In: Digital Technologies (DT), pp. 238–242 (2014)
Kamencay, P., Zachariasova, M., Hudec, R., Benco, M., Radil, R.: 3D image reconstruction from 2D CT slices. In: The True Vision - Capture, Transmission and Display of 3D Video, pp. 1–4 (2014)
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Huang, X., Hu, M. (2015). 3D Reconstruction Based on Model Registration Using RANSAC-ICP Algorithm. In: Pan, Z., Cheok, A., Mueller, W., Zhang, M. (eds) Transactions on Edutainment XI. Lecture Notes in Computer Science(), vol 8971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48247-6_4
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DOI: https://doi.org/10.1007/978-3-662-48247-6_4
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