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Hi all,

Thanks for taking your time to read this.

We have fixed stereo pairs of cameras looking into a closed volume. We know the dimensions of the volume and have the intrinsic and extrinsic calibration values for the camera pairs. The objective being to be able to identify the 3d positions of multiple duplicate objects accurately. Which naturally leads to what is described as the correspondence problem in litrature. We need a fast technique to match ball A from image 1 with Ball A from image 2 and so on. At the moment we use the properties of epipolar geomentry (Fundamental matrix) to match the balls from different views in a crude way and works ok when the objects are sparse, but gives a lot of false positives if the objects are densely scattered. Since ball A in image 1 can lie anywhere on a line going across image 2, it leads to mismatches when multiple objects lie on that line and look similar.

Is there a way to re-model this into a 3d line intersection problem or something? Since the ball A in image a can only take a bounded limit of 3d values, Is there a way to represent it as a line in 3d? and do a intersection test to find the closest matching ball in image 2?

Or is there a way to generate a sparse list of 3d values which correspond to each 2d grid of pixels in image 1 and 2, and do a intersection test of these values to find the matching objects across two cameras?

Because the objects can be identical, OpenCV feature matching algorithms like FLANN, ORB doesn't work.

Any ideas in the form of formulae or code is welcome.

Thanks! Sak

Hi all,

Thanks for taking your time to read this.

We have fixed stereo pairs of cameras looking into a closed volume. We know the dimensions of the volume and have the intrinsic and extrinsic calibration values for the camera pairs. The objective being to be able to identify the 3d positions of multiple duplicate objects accurately. Which naturally leads to what is described as the correspondence problem in litrature. We need a fast technique to match ball A from image 1 with Ball A from image 2 and so on. At the moment we use the properties of epipolar geomentry (Fundamental matrix) to match the balls from different views in a crude way and works ok when the objects are sparse, but gives a lot of false positives if the objects are densely scattered. Since ball A in image 1 can lie anywhere on a the epipolar line going across image 2, it leads to mismatches when multiple objects lie on that line and look similar.

Is there a way to re-model this into a 3d line intersection problem or something? Since the ball A in image a can only take a bounded limit of 3d values, Is there a way to represent it as a line in 3d? and do a intersection test to find the closest matching ball in image 2?

Or is there a way to generate a sparse list of 3d values which correspond to each 2d grid of pixels in image 1 and 2, and do a intersection test of these values to find the matching objects across two cameras?

Because the objects can be identical, OpenCV feature matching algorithms like FLANN, ORB doesn't work.

Any ideas in the form of formulae or code is welcome.

Thanks! Sak