Practical global optimization for multiview geometry
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This paper presents a practical method for finding the provably globally optimal solution to numerous problems in projective geometry including multiview triangulation, camera resectioning and homography estimation. Unlike traditional methods which may get trapped in local minima due to the non-convex nature of these problems, this approach provides a theoretical guarantee of global optimality. The formulation relies on recent developments in fractional programming and the theory of convex underestimators and allows a unified framework for minimizing the standard L 2-norm of reprojection errors which is optimal under Gaussian noise as well as the more robust L 1-norm which is less sensitive to outliers. Even though the worst case complexity of our algorithm is exponential, the practical efficacy is empirically demonstrated by good performance on experiments for both synthetic and real data. An open source MATLAB toolbox that implements the algorithm is also made available to facilitate further research.
Original language | English |
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Journal | International Journal of Computer Vision |
Volume | 79 |
Issue number | 3 |
Pages (from-to) | 271-284 |
Number of pages | 14 |
ISSN | 0920-5691 |
DOIs | |
Publication status | Published - Sep 2008 |
Externally published | Yes |
Bibliographical note
Funding Information:
Acknowledgements S. Agarwal and S. Belongie are supported by NSF-CAREER #0448615, DOE/LLNL contract no. W-7405-ENG-48 (subcontracts B542001 and B547328), and the Alfred P. Sloan Fellowship. M. Chandraker and D. Kriegman are supported by NSF EIA 0303622 & NSF IIS-0308185. F. Kahl is supported by Swedish Research Council (VR 2004-4579) & European Commission (Grant 011838, SMERobot).
- Branch and bound, Camera pose, Cameras, Geometry, Global optimization, Multiple view geometry, Reconstruction, Triangulation
Research areas
ID: 302051326