Hello,
Maybe my question is not really appropriate here.
As the function findFundamentalMat (with a RANSAC method) does not return the list of outliers, I was trying to use the fundamental matrix returned by the function to compute myself the reprojection error for the input points.
I looked into the source code and I discovered that there is a function called findInliers which call computeError to compute the error for the input points using the fundamental matrix estimated in the current iteration. When I checked this function:
const Point3f* from = m1.ptr<Point3f>();
const Point3f* to = m2.ptr<Point3f>();
const double* F = model.ptr<double>();
for(int i = 0; i < count; i++ )
{
const Point3f& f = from[i];
const Point3f& t = to[i];
double a = F[0]*f.x + F[1]*f.y + F[ 2]*f.z + F[ 3] - t.x;
double b = F[4]*f.x + F[5]*f.y + F[ 6]*f.z + F[ 7] - t.y;
double c = F[8]*f.x + F[9]*f.y + F[10]*f.z + F[11] - t.z;
errptr[i] = (float)std::sqrt(a*a + b*b + c*c);
}
for me model is the current estimated fundamental matrix which seems to have 12 elements instead of being a 3x3 matrix. Is there a problem or am I missing something ? Can someone explain me the formula for computing the error ?
To compute the error, I think I will use the distance error between the real 2d location in the image to the corresponding epipolar line using the fundamental matrix for each input points, but I want to understand what is behind the formula used in the source code of OpenCV.
Thanks.