Find corresponding positions in stereo rectified images on 3D pointCloud
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Meghana Dinesh
el 28 de Nov. de 2014
Comentada: Dima Lisin
el 2 de Dic. de 2014
In a 1000 X 1000 image (rectified stereo pair), I_left , I have features detected at positions:
P_left = 124.914794921875 155.905288696289
125.438186645508 206.946701049805
126.040199279785 258.120697021484
126.659095764160 309.096038818359
127.287139892578 360.007171630859.
I_right has the corresponding features detected at positions:
P_right = 73.1327972412109 155.756408691406
73.8827133178711 206.970779418945
74.3082046508789 258.157165527344
75.1555709838867 309.194702148438
76.6253433227539 360.086303710938
As you can see, the y positions of corresponding points are almost similar (slight difference may be due to calibration, but doesn't matter, I can take the mean and make my y values same, if necessary.)
After Rectification, I find disparity and reconstructScene , to get a 3D pointCloud.
From the 3D pointCloud, I want to find the 3D values of the 5 feature points positions I had detected in I_left and I_right.
How should I do this?
I am not able to map these positions detected in rectified images onto the point cloud.
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Dima Lisin
el 28 de Nov. de 2014
Hi Sanya,
reconstructScene returns pointCloud, which is an M-by-N-by-3 array. pointCloud(i, j, :) gives you x, y, and z coordinates corresponding to pixel i,j in the rectified image from camera 1.
4 comentarios
Dima Lisin
el 2 de Dic. de 2014
triangulate takes two sets of matching points, not just a single pair. It also returns the reprojection errors for the resulting 3D points, which lets you eliminate the bad matches.
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