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.
Poly- nomial eigenvalue solutions to minimal problems in com- puter vision.IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7):1381–1393
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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.