A new matrix-inversion-free solver reconstructs hidden-variable determinant polynomials for minimal problems using IFFT interpolation from samples and recovers solutions via rank-1 submatrices and Cramer's rule.
Sparse resultant-based minimal solvers in computer vision and their connection with the action matrix.Journal of Math- ematical Imaging and Vision, 66(3):335–360
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Solving Minimal Problems Without Matrix Inversion Using FFT-Based Interpolation
A new matrix-inversion-free solver reconstructs hidden-variable determinant polynomials for minimal problems using IFFT interpolation from samples and recovers solutions via rank-1 submatrices and Cramer's rule.