A Batch Reconstruction Algorithm of Multi-view Images Using Image Triplets

LU Jun; ZHANG Baoming; GUO Haitao; ZHANG Hongwei

doi:10.13203/j.whugis20140672

Volume 42 Issue 1

Jan. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(1): 109-115. > DOI: 10.13203/j.whugis20140672

LU Jun, ZHANG Baoming, GUO Haitao, ZHANG Hongwei. A Batch Reconstruction Algorithm of Multi-view Images Using Image Triplets[J]. Geomatics and Information Science of Wuhan University, 2017, 42(1): 109-115. DOI: 10.13203/j.whugis20140672

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A Batch Reconstruction Algorithm of Multi-view Images Using Image Triplets

1.
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450052, China

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The National 973 Program of China 2012CB720000

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Author Bio:
LU Jun, PhD candidate, lecturer, specializes in digital photogrammetry and computer vision. E-mail: ljhb45@126.com
Received Date: December 15, 2014
Published Date: January 04, 2017

Graphical Abstract

Abstract

Abstract

3D reconstruction of unordered multi-view images is very sensitive to noise. Error matching relations will affect the accuracy of the reconstruction or even lead to failure. A robust batch reconstruction algorithm is proposed in this paper, first the triplets which may contain mismatches are removed using closed cycle constraint, and then the trifocal tensor constraint in triplet is used instead of the epipolar constraint of the traditional algorithm, also the linear programming algorithm with the l_∞ norm is used instead of the second order cone programming to calculate a global optimum of rotations and locations of all the images. An efficient Union Find algorithm is introduced into the reconstruction to exact the multi-view matching points, and the 3D points are computed using the iterative linear triangulation. Experimental results show that the proposed method performs satisfactorily in terms of reconstruction efficiency and accuracy.
- image triplet,
- closed cycle constraint,
- batch reconstruction,
- union find,
- linear program

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[1]	Hartley R I, Trumpf J, Dai Y, et al. Rotation Averaging[J]. International Journal of Computer Vision, 2013, 103(3):267-305 doi: 10.1007/s11263-012-0601-0
[2]	Arie-Nachimson M, Kovalsky S Z, Kemelmacher-Shlizerman I, et al. Global Motion Estimation from Point Matches[C]. 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), Zurich, 2012
[3]	郭复胜, 高伟.基于辅助信息的无人机图像批处理三维重建方法[J].自动化学报, 2013, 39(6):834-845 doi: 10.1016/S1874-1029(13)60056-7 Guo Fusheng, Gao Wei. Batch Reconstruction from UAV Images with Prior Information[J]. Acta Automatica Sinica, 2013, 39(6):834-845 doi: 10.1016/S1874-1029(13)60056-7
[4]	Martinec D, Pajdla T. Robust Rotation and Translation Estimation in Multiview Reconstruction[C]. Computer Vision and Pattern Recognition, Minnesota, New York, 2007
[5]	Kahl F, Hartley R I. Multiple-view Geometry under the l∞-norm[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(9):1603-1617 doi: 10.1109/TPAMI.2007.70824
[6]	郑顺义, 张祖勋, 翟瑞芳.基于非量测相机的复杂物体三维重建[J].武汉大学学报·信息科学版, 2008, 33(5):446-449 http://ch.whu.edu.cn/CN/abstract/abstract1580.shtml Zheng Shunyi, Zhang Zuxun, Zhai Ruifang. 3D Reconstruction of Complex Objects Based on Non-metric Image[J]. Geomatics and Information Science of Wuhan University, 2008, 33(5):446-449 http://ch.whu.edu.cn/CN/abstract/abstract1580.shtml
[7]	Hartley R I, Zisserman A. Multiple View Geometry in Computer Vision[M]. New York:Cambridge University Press, 2003
[8]	Jiang Nianjuan, Cui Zhaopeng, Tan Ping. A Global Linear Method for Camera Pose Registration[C]. IEEE International Conference on Computer Vision, Sydney, 2013
[9]	Hartley R I, Sturm P. Triangulation[J]. Computer Vision and Image Understanding, 1997, 68(2):146-157 doi: 10.1006/cviu.1997.0547
[10]	Moulon P, Monasse P, Marlet R. Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion[C]. IEEE International Conference on Computer Vision, Sydney, 2013
[11]	Fasano P, Forrest J. The COIN-OR Open Solver Interface:Technology Overview[EB/OL].https://projects.coin-or.org/Osi,2014
[12]	Galler B A, Fischer M J. An Improved Equivalence Algorithm[J]. Communications of the ACM, 1964, 7(5):301-303 doi: 10.1145/364099.364331
[13]	Snavely, Seitz S M, Szeliski R. Modeling The World from Internet Photo Collections[J]. International Journal of Computer Vision, 2008, 80(2):189-210 doi: 10.1007/s11263-007-0107-3
[14]	Lourakis M I A, Argyros A A. Sba:A Software Package for Generic Sparse Bundle Adjustment[J]. ACM Transactions on Mathematical Software, 2009, 36(1):1-30 https://dl.acm.org/purchase.cfm?id=1486527
[15]	Strecha C, van Hansen W, van Gool L J, et al. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery[C].Computer Vision and Pattern Recognition, Alaska, New York, 2008
[16]	Lowe D. Distinctive Image Features from Scale-Invariant Keypoints[J]. International journal of computer vision, 2004, 60(2):91-110 doi: 10.1023/B:VISI.0000029664.99615.94
[17]	Nistér D. An Efficient Solution to the Five-Point Relative Pose Problem[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(6):756-770 doi: 10.1109/TPAMI.2004.17
[18]	Wu C. Towards Linear-Time Incremental Structure from Motion[C].3DTV-Conference, Aberdeen, 2013
[19]	Sinha S, Steedly D, Szeliski R. A Multi-stage Linear Approach to Structure from Motion[M]. Berlin:Springer, 2012

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