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This homework is split into two weeks.
Having a fundamental matrix F and camera calibration K, the essential matrix E can be computed from the equation
F
K
E
<latex>F = K^{-\top} E K^{-1}\,.</latex>
First, matrix E1 is computed as:
E1
E1 = K' * F * K;
Due to errors in data used to estimate F and/or K, the obtained matrix E1 is not a true essential matrix, its two non-zero singular values are not equal. So the matrix must be modified in order to make these singular values equal:
[U D V] = svd( E1 ); D(2,2) = D(1,1); E = U * D * V';
To summarize, starting with regular matrix G obtained from the 8-point algorithm, we apply SVD two times. First, G is decomposed, the smallest singular value zeroed and F is composed. Secondly, K is applied, E1 is decomposed and the two singular values are made equal giving raise to essential matrix E. Finally, K can be applied to compute a fundamental matrix Fe consistent with K from E.
G
Fe
Find two essential matrices. A possibly bad Ex and the best E
Ex
Fx
09_egx.pdf
d1_i
d2_i
09_errorsx.pdf
09_eg.pdf
09_errors.pdf
u1
u2
point_sel
09a_data.mat
R
C
P1
P2
X
P_i
09_reprojection.pdf
09_errorsr.pdf
09_view1.pdf
09_view2.pdf
09_view3.pdf
09b_data.mat
The first part: upload an archive consisting of:
hw09a.m
The second part: upload an archive consisting of:
hw09b.m