On Degeneracy of Linear Reconstruction From Three Views: Linear Line Complex and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Brightness Constraints: On Direct Estimation of Structure and Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel View Synthesis by Cascading Trilinear Tensors
IEEE Transactions on Visualization and Computer Graphics
Balanced Recovery of 3D Structure and Camera Motion from Uncalibrated Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Minimal Set of Constraints for the Trivocal Tensor
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Tensor Embedding of the Fundamental Matrix
SMILE'98 Proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Trifocal Tensors with Grassmann-Cayley Algebra
RobVis '01 Proceedings of the International Workshop on Robot Vision
Affine Reconstruction from Translational Motion under Various Autocalibration Constraints
Journal of Mathematical Imaging and Vision
A 3D Shape Constraint on Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action recognition using subtensor constraint
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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This article deals with the problem of recovering the three trifocal tensors between three views from a set of point correspondences. We give a new way of deriving the trifo cal tensor based on Grassmann-Cayley algebra that sheds some new light on its structure and leads to a complete characterization of its geometric and algebraic prop erties which is fairly intuitive, i.e. geometric. We give a set of algebraic constraints satisfied by the 27 coefficients of the trifocal tensor which allow to parameterize it minimally with 18 c oefficients. We then describe a robust method for estimating the trifocal tensor from point and line correspondences that uses this minimal parameterization. Experimental results show that this method is superior to the linearmethods which had been previously published.