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Im working on a 3d reconstruction project where i have trouble matching the features in order to proceed with the reconstruction. To be more specific when im matching feature of matlab's examples images i have a high correct to wrong matches ratio but when im matching features of my own photos taken by a phone camera i have almost only wrong matches. I 've tried tuning the threshold but the problem still remains. Any ideas/sugestions of what is going wrong?

The descriptor im using is the sift descriptor from the vlfeat toolbox

edit: here is a dropbox link with the original images, the detected salient/corner points and the matches.

Yiannis Ath
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  • I assume it is a combination of it being explicitly trained for the example images and your phone images are of a lower quality. Could you share any links to the example images and your phone images? – pteronewone Mar 03 '17 at 03:17
  • @pteronewone i eddited the question and included a dropbox link with the images – Yiannis Ath Mar 03 '17 at 11:00
  • Please always add the images to the question itself, links get expired. – MeiH Aug 16 '22 at 01:36

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I think your main problems here are significant difference in lighting between the images, and specular reflections off the plastic casing. You are also looking at the inside of the USB drive through the transparent plastic, which doesn't help.

What feature detectors/descriptors have you tried? I would start with SURF, and then I would try MSER. It is also possible to use multiple detectors and descriptors, but you should be careful to keep them separate. Of course, there are also lots of parameters for you to tune.

Another thing that may be helpful is to take higher-resolution images.

If you are trying to do 3D reconstruction, can you assume that the camera does not move much between the images? In that case, try using vision.PointTracker to track points from one frame into the other instead of matching them.

Dima
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