International Journal of Computer Vision
Weakly-calibrated stereo perception for rover navigation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Convex Optimization
Quasiconvex Optimization for Robust Geometric Reconstruction
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiple View Geometry and the L_"-norm
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Removing Outliers Using The L\infty Norm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Recovering Camera Motion Using L\infty Minimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Predicting and estimating the accuracy of n-occular optical tracking systems
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Optimal algorithms in multiview geometry
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Visibility probability structure from sfm datasets and applications
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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It has been shown that various geometric vision problems such as triangulation and pose estimation can be solved optimally by minimizing L∞ error norm. This paper proposes a novel algorithm for sequential estimation. When a measurement is given at a time instance, applying the original batch bi-section algorithm is very much inefficient because the number of seocnd order constraints increases as time goes on and hence the computational cost increases accordingly. This paper shows that, the upper and lower bounds, which are two input parameters of the bi-section method, can be updated through the time sequence so that the gap between the two bounds is kept as small as possible. Furthermore, we may use only a subset of all the given measurements for the L∞ estimation. This reduces the number of constraints drastically. Finally, we do not have to reestimate the parameter when the reprojection error of the measurement is smaller than the estimation error. These three provide a very fast L∞ estimation through the sequence; our method is suitable for real-time or on-line sequential processing under L∞ optimality. This paper particularly focuses on the triangulation problem, but the algorithm is general enough to be applied to any L∞ problems.