CODEWITHSUNDEEP
matlab3dcomputer-visionmatlab-cvstdisparity-mapping
User1234321232
User1234321232

Reputation: 547

Disparity calculation of two similar images in matlab

I have two images(both are exactly same images) and I am trying to calculate the disparity between them using sum of squared distances and reconstruct disparity in 3D space. Do I need to rectify the image before calculating disparity?

The following are the steps that I have done so far for disparity map computation(I have tried with rectification and without rectification but both are returning all zeroes disparity matrix). 

For each pixel in the left image X, 
   Take the pixels in the same row in the right image.
   Separate the row in right image to windows.
   For each window,
     Calculate the disparity for each pixel in that window with X
     Select the pixel in the window which gives minimum SSD with X
   Find the pixel with minimum disparity among all windows as the best match to X

Am I doing it correctly?

How can I visualise the 3D reconstruction of the disparity as scatter plot in matlab?

Upvotes: 1

Views: 2665

Answers (2)

Vlad
Vlad

Reputation: 4515

Rectification guarantees that matches are to be found in the same row (for horizontally separated cameras). If you have doubts about rectification of your images you can try to compare rows by drawing horizontal lines between horizontally separated images. If the lines hit the same features you are fine, see the picture below where images are NOT rectified. The fact that they are distorted means there was a lens distortion correction as well as attempted (but not actually performed correctly) rectification.

Now, let’s see what you meant by the same images. Did you mean the images of the same object that were taken from different viewpoints? Note that if the images are literally the same (the same viewpoints) the disparity will be zero as was noted in another answer. The definition of disparity (for horizontally separated cameras) is a value of shift (in the same row) between matching features. The disparity is related to depth (if optical axes of cameras are parallel) as disparity d=f*B/z, where z - depth, B - baseline or separation between cameras and f is a focal length. You can transform the formula above into disparity/B=f/z which basically says that disparity related to camera separation as focal length is related to distance. In other words, the ratios of horizontal and distance measures are equal.

enter image description here

If your images are taken with the cameras shifted horizontally the disparity (in a simple correlation algorithm) is typically calculated in 5-embedded loops:

loop over image1 y  
   loop over image1 x  
      loop over disparity d  
         loop over correlation window y  
            loop over correlation window x

Disparity, or D_best, gives you the best matching window between image1 and image2 across all possible values of d. Finally, scatterplots are for 3D point clouds while disparity can be rather visualized as a heat color map. If you need to visualize 3D reconstruction or simply saying a 3D point cloud calculate X, Y, Z as: Z=fB/D, X=uZ/f, Y=v*Z/f, where u and v are related to column and row of wxh image as u=col-w/2 and v=h/2-row, that is u, v form an image centered coordinate system.

Upvotes: 2

Dima
Dima

Reputation: 39389

If your two images are exactly the same, then the disparity would be 0 for every pixel. You either have to use two separate cameras to take the images, or take them with a single camera from two different locations. The best way to do 3D reconstruction is to use a calibrated stereo pair of cameras. Here is an example of how to do that using the Computer Vision System Toolbox for MATLAB.

Upvotes: 0

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