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Keypoint detectors

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Result

Sr	Scene	Harris	Shi-Tomasi	FAST	SIFT	ORB	AKAZE	BRISK
1	scuba diver and fish · edges 12%	0.981	0.759	0.793	0.829	0.902	0.891	0.768
2	polar bear on a rail · edges 18%	0.985	0.865	0.806	0.823	0.674	0.804	0.740
3	glass pyramid · edges 23%	0.909	0.846	0.740	0.713	0.846	0.878	0.731
4	coiled snake · edges 30%	0.921	0.857	0.754	0.727	0.816	0.873	0.721

The finding

Harris does not win by finding more points — but it does concentrate them

Harris returns ~1000 keypoints and AKAZE ~357. More keypoints means more chances for one to land inside the 3 px match radius, so the natural objection is that Harris wins on density. Randomly discarding its keypoints answers it:

Flat. A tenth of the keypoints scores the same as all of them. Randomly subsampled, so the survivors are not quietly reselected for quality. Pinned by test_harris_does_not_win_by_finding_more_keypoints.

So density is not the explanation — but placement partly is. Harris's 33.5% coverage against SIFT's 80.6% says the two are not solving the same problem: Harris is answering "where are the most reliable corners", and a homography needs "where are reliable corners *spread across the frame*". Pinned by test_harris_trades_frame_coverage_for_repeatability.

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Figures

Elsewhere

Code and full results on GitHub (code private)

Every number is read from the file the run produced. github.com/hammasbuilds · kaggle.com/muhammadhammas13