Paper Info
Title
Practical global optimization for multiview geometry
Abstract
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 L2-norm of reprojection errors which is optimal under Gaussian noise as well as the more robust L1-norm which is less sensitive to outliers. The efficacy of our algorithm 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.
Year
DOI
Venue
2006
10.1007/11744023_46
ECCV
Keywords
Field
DocType
optimal solution,global optimality,multiview geometry,multiview triangulation,homography estimation,good performance,practical global optimization,camera resectioning,convex underestimators,non-convex nature,fractional programming,gaussian noise,projective geometry,branch and bound,global optimization
Mathematical optimization,Global optimization,Computer science,Projective geometry,Computational geometry,Algorithm,Homography,Camera resectioning,Triangulation (social science),Geometry,Gaussian noise,Fractional programming
Conference
Volume
ISSN
ISBN
3951
0302-9743
3-540-33832-2
Citations 
PageRank 
References 
33
2.11
10
Authors
5
Name
Order
Citations
PageRank
Sameer Agarwal110328478.10
Manmohan Chandraker245125.58
Fredrik Kahl3141592.61
David Kriegman47693451.96
Serge J. Belongie5125121010.13