Structure from motion using line correspondences
International Journal of Computer Vision
Robust regression methods for computer vision: a review
International Journal of Computer Vision
Lines and Points in Three Views and the Trifocal Tensor
International Journal of Computer Vision
On Degeneracy of Linear Reconstruction From Three Views: Linear Line Complex and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algebraic Functions For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Reference Frames to Reference Planes: Multi-View Parallax Geometry and Applications
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A Common Framework for Multiple View Tensors
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Concerning Bayesian Motion Segmentation, Model, Averaging, Matching and the Trifocal Tensor
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Trilinear Tensor: The Fundamental Construct of Multiple-view Geometry and Its Applications
AFPAC '97 Proceedings of the International Workshop on Algebraic Frames for the Perception-Action Cycle
On the geometry and algebra of the point and line correspondences between N images
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Trilinearity of three perspective views and its associated tensor
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Matching constraints and the joint image
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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It is known that recovering projection matrices from planar configurations is ambiguous, thus, posing the problem of model selection -- is the scene planar (2D) or non-planar (3D)? For a 2D scene one would recover a homography matrix, whereas for a 3D scene one would recover the fundamental matrix or trifocal tensor. The task of model selection is especially problematic when the scene is neither 2D nor 3D -- for example a "thin" volume in space. In this paper we show that for certain tasks, such as reprojection, there is no need to select a model. The ambiguity that arises from a 2D scene is orthogonal to the reprojection process, thus if one desires to use multilinear matching constraints for transferring points along a sequence of views it is possible to do so under any situation of 2D, 3D or "thin" volumes.