Disparity map中的OpenCv深度估计

Question

I'm trying to estimate depth from a stereo pair images with OpenCV. I have disparity map and depth estimation can be obtained as:

             (Baseline*focal)
depth  =     ------------------
           (disparity*SensorSize)

I have used Block Matching technique to find the same points in the two rectificated images. OpenCV permits to set some block matching parameter, for example BMState->numberOfDisparities .

After block matching process:

cvFindStereoCorrespondenceBM( frame1r, frame2r, disp, BMState);
cvConvertScale( disp, disp, 16, 0 );
cvNormalize( disp, vdisp, 0, 255, CV_MINMAX );

I found depth value as:

if(cvGet2D(vdisp,y,x).val[0]>0)
   {
   depth =((baseline*focal)/(((cvGet2D(vdisp,y,x).val[0])*SENSOR_ELEMENT_SIZE)));
   }

But the depth value obtaied is different from the value obtaied with the previous formula due to the value of BMState->numberOfDisparities that changes the result value.

How can I set this parameter? what to change this parameter?

Thanks

Answer 1

The simple formula is valid if and only if the motion from left camera to right one is a pure translation (in particular, parallel to the horizontal image axis).

In practice this is hardly ever the case. It is common, for example, to perform the matching after rectifying the images, ie after warping them using a known Fundamental Matrix, so that corresponding pixels are constrained to belong to the same row. Once you have matches on the rectified images, you can remap them onto the original images using the inverse of the rectifying warp, and then triangulate into 3D space to reconstruct the scene. OpenCV has a routine to do that: reprojectImageTo3d

Answer 2

The formula you mentioned above wont work as the camera plane and the image plane is not same ie the camera will be situated at some height and the plane it captures will be on the ground. So, you have to do a little modification in this formula. You can fit these disparity values and known distance on a polynomial by curve fitting .From it you will get the coefficients which can be used for other unknown distances. 2nd way is to create a 3d Point Cloud using wrap matrix and reprojectimageTo3d (Opencv API) .