3D Reconstruction Based on Hexagonal Pixel’s Dense Stereo Matching

Ze Tao Jiang; Qing Hui Xiao; Ling Hong Zhu

doi:10.4028/www.scientific.net/AMM.20-23.487

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 20-233D Reconstruction Based on Hexagonal Pixel’s Dense...

3D Reconstruction Based on Hexagonal Pixel’s Dense Stereo Matching

Abstract:

A new feature points extraction method is presented, which consider pixel as hexagonal. The method quasi increases the density of image pixel, expands the dynamic range of feature point extraction, increases the number of the features and resolves the problem of deformation of reconstruction which was leaded by lack of feature points. Firstly, the method was successful applied to sift operator of features extraction in this paper and then use dense stereo matching method to find the matching point of the image sequences. Secondly, through the RANSAC method to eliminate mistake matches, and by the camera matrix, calculate the corresponding points’ three-dimensional coordinates of space. Finally, the 3D model can be established through the partition merging triangulation method and texture mapping. Experimental results show that this method can get more accurate matches pairs and achieve a satisfactory effect of 3D reconstruction.

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Periodical:

Applied Mechanics and Materials (Volumes 20-23)

Pages:

487-492

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.20-23.487

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Online since:

January 2010

Authors:

Ze Tao Jiang, Qing Hui Xiao, Ling Hong Zhu

Keywords:

Dense Stereo Matching, Hexagonal, RANSAC, Reconstruction, SIFT

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References

[1] C. Harris, M. Stephens, A Combined Corner and Edge Detector. In: Proceedings of the Alvey Vision Conference, 1988: 189-192.

DOI: 10.5244/c.2.23

Google Scholar

[2] Lowe, D.G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, preprint (2004).

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[3] KOENDERIN KJ, The structure of images [J] Biological Cybernetics, (1984).

Google Scholar

[4] LINDEBERG T, Sscale-space for discrete signals [J]. I EEE T ransactions PA MI , (1980).

Google Scholar

[5] Yichen Wei, Eyal Ofek, Long Quan and Heung YeungSllum, Modeling Hair form Multiple Views, SIGGRAPH, (2005).

Google Scholar

[6] Zheng Bina, Jiang Zetao, WuMin, 3D imensional reconstruction based on image dense stereo matching, CHINESE JOURNAL OF STEREOLO GY AND IMAGE ANALYS IS Vo. l 14 No. 2, (2009).

Google Scholar

[7] Maxime Lhuillier, Long Quan, A Quasi-Dense Approach to Surface Reconstruction from Uncalibrated Images, IEEE Transaction on PAMI, VOL. 27, NO. 3, (2005).

DOI: 10.1109/tpami.2005.44

Google Scholar

[8] TANG Li, WU ChengKe, LIU ShiGang, YAN YaoPing, Image Dense Stereo Matching by Technique of Region Growing, CHINESE JOURNAL OF COMPU TERS, (2004).

Google Scholar

[9] Chao jun, ZOU Beiji, WANG Lei, PENG Xiaoning, Implicit surfaces triangulation method with boundary preserved, Computer Engineering and Design, (2009).

Google Scholar

[10] Information on http: /people. cs. ubc. ca/~lowe/keypoints.

Google Scholar