Andrew26's profile - activity

Thanks for your answer, but I think that my question was formulated bad... Actually, the problem is not to apply DenseFeatureDetector, but to do matching using it. In the case of use of DenseFeatureDetector in 2 images, it creates a sort of grid on both, but it can't correctly find matches (I used SIFT descriptors). I think that some preliminary image treatment sould be done, like rectification or centering... So, I'm looking for existing examle where this technique has already been implemented.

Hello

I'm trying to obtain dense 3D reconstruction. Using SIFT or SURF detector/descriptor one can obtain only sparse reconstruction which can then be upgrated using dense matching (using DenseFeatureDetector). Unfortunately, I could not find any examples of its implementation. Could someone give me an example ? Should I rectify images before ?

Thank you

Hello

I'm trying to verify if the projective reconstruction that I've estimated is correct. I have an image dataset with given values of camera matrices Pm(i) (for metric reconstruction). Then, I found camera matrices P(i) using an algorithm of projective factorization.

From Hartley and Zisserman (p.460) we know that : Pm(i) = P(i)*H

So, I can find H corresponding using the pseudoinverse of P(1) : H = pinv(P(1))*Pm(1)

Than, by multilpying all others P(i) by H, I should obtain all others Pm(i). Is this approach correct? Thanks in advance!