Efficient Linear Solution of Exterior Orientation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Fundamental Matrix via Constrained Least-Squares: A Convex Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel View Synthesis by Cascading Trilinear Tensors
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D reconstruction in a constrained camera system
Pattern Recognition Letters
On the Estimation of the Fundamental Matrix: A Convex Approach to Constrained Least-Squares
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Using Points at Infinity for Parameter Decoupling in Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Recovering Multiple View Geometry from Mutual Projections of Multiple Cameras
International Journal of Computer Vision
Relative Pose Estimation of Surgical Tools in Assisted Minimally Invasive Surgery
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Computer Vision and Image Understanding
Camera pose estimation based on angle constraints
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Algebraic error analysis of collinear feature points for camera parameter estimation
Computer Vision and Image Understanding
Generating free viewpoint images from mutual projection of cameras
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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This paper gives a widely applicable technique for solving many of the parameter estimation problems encountered in geometric computer vision. A commonly used approach in such parameter minimization is to minimize an algebraic error function instead of a possibly preferable geometric error function. It is claimed in this paper, however, that minimizing algebraic error will usually give excellent results, and in fact the main problem with most algorithms minimizing algebraic distance is that they do not take account of mathematical constraints that should be imposed on the quantity being estimated. This paper gives an efficient method of minimizing algebraic distance while taking account of the constraints. This provides new algorithms for the problems of resectioning a pinhole camera, computing the fundamental matrix, and computing the tri-focal tensor.