Comments (3)
@Grungeby52 great question. I assume your ORB keypoints are based on OpenCV.
ORB uses FAST keypoints under the hood and filters them with TopN point selection based on keypoint strength before extracting the ORB descriptor. Several ideas come to mind:
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In order to use ANMS algorithms in this repo without modifying too much code, you'd need to return many more points from the ORB detector and then apply ANMS on those. This is not practical if speed is important.
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Disabling TopN filtering would be another option but it would require modifying OpenCV core functionality. Not recommended.
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The most practical solution is to extract FAST keypoints manually, filter them using ANMS, and extract ORB descriptors for selected keypoints. This is exactly what we did for experiments in the paper (Section 4.5: Application to SLAM)
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Hello,
Thanks for quick feedback,
As far as I understand, you are telling me to homogenize the key point I got with FAST in the 3rd recommendation. Did I get it right?
Best Regards,
Onur Güzeldemirci
from anms-codes.
@Grungeby52, yeah, I'd recommend using the 3rd option: get FAST points, homogenize them with ANMS, and then extract ORB descriptor for the selected points.
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Related Issues (11)
- "RANDR" missing on display HOT 1
- Python version mistake HOT 1
- Get exact number of points HOT 2
- Error C2334 unexpected token(s) preceding '{'; skipping apparent function body HOT 2
- Is anms rotation and/or scale invariant? HOT 1
- Getting std::bad_alloc error when using SSC function HOT 4
- std::bad_alloc in anms.h HOT 1
- Effectiveness for monocular SLAM HOT 2
- expression for lower bound al of binary search HOT 3
- Response vector HOT 1
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