triangulate 3d points from stereo images?

edit

I have a stereo pair of images, downloaded from a dataset along with the camera calibration data.

I am using the camera projection matrices, which look like this:

6.452401 0.000000 6.359587 0.000000 
0.000000 6.452401 1.941291 0.000000
0.000000 0.000000 1.000000 0.000000


6.452401 0.000000 6.359587 -3.682632 
0.000000 6.452401 1.941291 0.000000 
0.000000 0.000000 1.000000 0.000000

I have feature matched keypoints on both frames, which I then run through triangulatepoints, like this:

vector<cv::Point2f> points2dLeft, points2dRight;

cv::Mat points_3D(1, 5, CV_64FC4);


for (std::vector<cv::DMatch>::const_iterator it = good_matches.begin(); it != good_matches.end(); ++it)
{

    float x = keypoints1[it->queryIdx].pt.x;
    float y = keypoints1[it->queryIdx].pt.y;
    points2dLeft.push_back(cv::Point2f(x, y));

    x = keypoints2[it->trainIdx].pt.x;
    y = keypoints2[it->trainIdx].pt.y;
    points2dRight.push_back(cv::Point2f(x, y));
}


cv::triangulatePoints(P1, P2, points2dLeft2, points2dRight2, points_3D);

These are the 2d points:

http://s000.tinyupload.com/download.p... http://s000.tinyupload.com/download.p...

and this is the camera data:

http://www.mrt.uni-karlsruhe.de/geige...

I have also tried the method here:

http://www.morethantechnical.com/2012...

which gives different,, but just as awful results. Has anyone successfully done this and created an accurate point cloud?