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IEICE Transactions on Information and Systems 2005 E88-D(8):2001-2004; doi:10.1093/ietisy/e88-d.8.2001
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Copyright © 2005 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Letters -- Image Recognition, Computer Vision

Robust 3D Reconstruction with Outliers Using RANSAC Based Singular Value Decomposition

Xi LI1, Zhengnan NING1 and Liuwei XIANG1

1 The authors are with the Xi'an Jiaotong University, Xi'an, Shannxi province, 710049 China. E-mail: xli{at}aiar.xjtu.edu.cn

It is well known that both shape and motion can be factorized directly from the measurement matrix constructed from feature points trajectories under orthographic camera model. In practical applications, the measurement matrix might be contaminated by noises and contains outliers. A direct SVD (Singular Value Decomposition) to the measurement matrix with outliers would yield erroneous result. This paper presents a novel algorithm for computing SVD with outliers. We decompose the SVD computation as a set of alternate linear regression subproblems. The linear regression subproblems are solved robustly by applying the RANSAC strategy. The proposed robust factorization method with outliers can improve the reconstruction result remarkably. Quantitative and qualitative experiments illustrate the good performance of the proposed method.

Key Words: structure from motion, outlier, SVD, linear regression, RANSAC

Manuscript received January 18, 2005. Manuscript revised April 14, 2005.


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