Computer Vision Exercise Session 4 (Discussions) Institute of - - PowerPoint PPT Presentation

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Computer Vision Exercise Session 4 (Discussions) Institute of - - PowerPoint PPT Presentation

Computer Vision Exercise Session 4 (Discussions) Institute of Visual Computing SIFT feature extraction You want lot of discriminative features to get matches. It is a bad idea to increase the threshold to be able to plot


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Institute of Visual Computing

Computer Vision

Exercise Session 4 (Discussions)

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Institute of Visual Computing

SIFT feature extraction

  • You want lot of discriminative features to get

matches.

  • It is a bad idea to increase the threshold to be

able to plot correspondences.

  • Just plot a subset of the putative matches.
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Institute of Visual Computing

SIFT feature extraction and scene choice

  • This is a perfect scene :
  • You get a lot of SIFT features, exactly where you expect

them to be. Good job Stefan Brugger!

Inliers with threshold = 0.1 pix (Sampson)

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Institute of Visual Computing

RANSAC – the big picture

  • It is meant to find small set of good inliers out of the

noise: You better have lots of putative correspondence !

  • lots of iteration (e.g. 10 000) is fully normal! (it’s quick in c++)
  • The threshold has to be understood together with the

distance.

  • 2 pixels for the sum of distances is equal to 1 pixel of average

reprojection error but not for Sampson distance.

  • We are filtering correspondences:

i.e. We do not mark points as outliers, but correspondences.

You always need to re-estimate F/E on all inliers !

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Institute of Visual Computing

RANSAC – adaptative

  • This is what we want to to, since we can not set the number
  • f iteration other way,
  • (1000 is really too arbitrary and too low !)
  • Do not stop at 1000 iteration(!!)
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Institute of Visual Computing

5pt-RANSAC Advantage of taking less samples

  • It helps a lot for small inlier ratios.
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Institute of Visual Computing

5pt-RANSAC

  • Please make sure to compute the Sampson distance with F

and un-normalized points

  • Refine E on inliers using your essentialMatrix from Ex2

(calibrated_fivepoints does not work with more than 5 input, you can check the code of calibrated_fivepoint_helper.c)

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Institute of Visual Computing

Decompose E

  • It could be that you still have wrong matches in

your inlier set. Those could be behind the camera.

  • To select which of the 4 configurations is good, take

the one with most matches in front.