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

Xi LI  Zhengnan NING  Liuwei XIANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E88-D   No.8   pp.2001-2004
Publication Date: 2005/08/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.8.2001
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
structure from motion,  outlier,  SVD,  linear regression,  RANSAC,  

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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.