Stereo matching

1
votes

I am using Camera Calibration Toolbox for Matlab. After calibration I have intrinsic and extrinsic parameters of stereo camera system. Next, I would like to determine the distance between the camera system and the object. To get this information, I used the function stereo_triangulation which is included in the Toolbox. Input are two matrixes including pixel coordinates of correspondences in the left and right image.

I tried to get coordinates of correspondences with using of Basic Block Matching method which is described in Matlab's help for Stereo Vision.

Resolution of my pictures is 1280x960 pixels. I know that the biggest disparity is around 520 pixels. I set the maximum of disparity range to 520. But then determine the coordinates takes ages. It is not possible use in practice. Calculating of disparity map is much faster with using of Matlab's function disparity(). But I want the step before - coordinates of correspondences.

Please can you suggest how can I effectively get the coordinates with Matlab?

1
matlabstereoscopy

1 Answers

1
votes

Disparity and 3D are related by simple formulas (see below) so the time for calculating 3D data and disparity map should be the same. The notation is

  • f - focal length in pixels,
  • B - separation between cameras,
  • u, v - row and column in the system centered on the middle of the image,
  • d-disparity,
  • x, y, z - 3D coordinates.

z=f*B/d;
x=z*u/f;
y=z*v/f;

1280x960 is too large resolution for any correlation stereo to work in real time. Think about it: you have to loop over a 2d image, over 2d correlation window and over the range of disparities. This means 5 embedded loops! I don't work with Matlab anymore but I know that it is quite slow.