Computer Vision and Image Understanding
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
How Hard is 3-View Triangulation Really?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Uncertainty Models in Quasiconvex Optimization for Geometric Reconstruction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image Noise Induced Errors in Camera Positioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Displacement via Constrained Minimization of the Algebraic Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast optimal algorithm for L2 triangulation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Fast optimal three view triangulation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Visual Servoing via Advanced Numerical Methods
Visual Servoing via Advanced Numerical Methods
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This paper addresses multiple-view L2 triangulation by proposing a new method based on eigenvalue problems (EVPs), which belong to the class of convex programming. The proposed method provides a candidate of the sought 3D point and a straightforward condition for establishing its optimality, which also yields a guaranteed range for the optimal cost of the triangulation problem in case of non-optimality. The proposed method is illustrated through some well-known examples with real data, for which the provided candidate 3D point is always optimal. These examples also show that the computational time of the proposed method is indeed small and competitive with existing approaches.