Hi,
I am currently facing a problem, to depict you what my programm does and should do, here is the copy/paste of the beginning of a previous post I've made.
This program lies on the classic "structure from motion" method.
The basic idea is to take a pair of images, detect their keypoints and compute the descriptors of those keypoints. Then, the keypoints matching is done, with a certain number of tests to insure the result is good. That part works perfectly.
Once this is done, the following computations are performed : fundamental matrix, essential matrix, SVD decomposition of the essential matrix, camera projection matrices computation and finally, triangulation.
The result for a pair of images is a set of 3D coordinates, giving us points to be drawn in a 3D viewer. This works perfectly, for a pair.
However, I have to perform a step manually, and this is not acceptable if I want my program to efficiently work with more than two images.
Indeed, I compute my projection matrices according the classic method, as follows, at paragraph "Determining R and t from E" : https://en.wikipedia.org/wiki/Essential_matrix
I have then 4 possible solutions for my projection matrix.
I think I have understood the geometrical point of view of the problem, portrayded in this Hartley and Zisserman paper extract (chapters 9.6.3 and 9.7.1) : http://www.robots.ox.ac.uk/~vgg/hzbook/hzbook2/HZepipolar.pdf
Nonetheless, my question is : **Given the four possible projection matrices computed and the 3D points computed by the OpenCV function triangulatePoints() (for each projection matrix), how can I elect the "true" projection matrix, automatically ?** (without having to draw 4 times my points in my 3D viewer, in order to see if they are consistent)
Thanks for reading.
↧