A convex method to minimal problems for fundamental matrix estimation with radial distortion

Zuoluo Zhang; Zongqing Lu; Qingmin Liao

doi:10.1117/12.2503142

9 August 2018 A convex method to minimal problems for fundamental matrix estimation with radial distortion

Zuoluo Zhang, Zongqing Lu, Qingmin Liao

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108061C (2018) https://doi.org/10.1117/12.2503142
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

This paper focuses on the problem of estimating the fundamental matrix with unknown radial distortion. The general method to the problem is Gröbner basis method. That solves nontrivial polynomial equations formed by a pair of correspondences under one-parameter division model for radial distortion, which is nonconvex and no noise-resistant. Using results from polynomial optimization tools and rank minimization method, this paper shows that the problem can be solved as a sequence of convex semi-definite programs. In the experiments, we show that the proposed method works well and is more noise-resistant.

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Zuoluo Zhang, Zongqing Lu, and Qingmin Liao "A convex method to minimal problems for fundamental matrix estimation with radial distortion", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061C (9 August 2018); https://doi.org/10.1117/12.2503142

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KEYWORDS

Distortion

Cameras

3D modeling

Algorithm development

Data modeling

Imaging systems

Optimization (mathematics)

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