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Mastering OpenCV 3

You're reading from   Mastering OpenCV 3 Get hands-on with practical Computer Vision using OpenCV 3

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Product type Paperback
Published in Apr 2017
Publisher
ISBN-13 9781786467171
Length 250 pages
Edition 2nd Edition
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Authors (6):
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Shervin Emami Shervin Emami
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Shervin Emami
David Millán Escrivá David Millán Escrivá
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David Millán Escrivá
Eugene Khvedchenia Eugene Khvedchenia
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Eugene Khvedchenia
Daniel Lelis Baggio Daniel Lelis Baggio
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Daniel Lelis Baggio
Roy Shilkrot Roy Shilkrot
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Roy Shilkrot
Jason Saragih Jason Saragih
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Jason Saragih
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Toc

Reconstructing the scene


Next, we look into the matter of recovering the 3D structure of the scene from the information we have acquired so far. As we had done before, we should look at the tools and information we have at hand to achieve this. In the preceding section, we obtained two camera matrices from the essential matrix; we already discussed how these tools would be useful for obtaining the 3D position of a point in space. Then, we can go back to our matched point pairs to fill in our equations with numerical data. The point pairs will also be useful in calculating the error we get from all our approximate calculations.

This is the time to see how we can perform triangulation using OpenCV. Luckily, OpenCV supplies us with a number of functions that make this process easy to implement: triangulatePoints, undistortPoints, and convertPointsFromHomogeneous.

Remember we had two key equations arising from the 2D point matching and P matrices: x=PX and x'= P'X, where x and x' are matching...

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