What do a real matcher's mismatches look like: random pixels, near misses, or along the epipolar line?

What it found

sensing-epigate injected mismatches of three invented kinds. This project measures them: OpenCV SIFT on the Middlebury 2014 Motorcycle stereo pair (truth from its disparity map) and on ten scikit-image photographs warped by known homographies, with every wrong match classed as along-epipolar, near (within 40 px) or far. With no ratio test 56% of stereo matches are wrong, 84% of those far; Lowe's ratio test removes the random-looking ones first, so at 0.7 only 6% are wrong but 62% of those lie within 2 px of the true epipolar line (random keypoints land there 1% of the time), the case a single-pair epipolar test cannot reject. Warped photographs: 40% wrong without a ratio test, 2% at 0.6, survivors 12–19% near. Limits: one stereo pair and one detector, ground truth good to about a pixel, class boundaries are mine, not fed back into bundle adjustment.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.