Keypoint matching by outlier pruning with consensus constraint
Bibliographic record
Abstract
A simple and reliable keypoint matching method is proposed in this paper. Our research is motivated by the desire to improve the performance of multi-view geometry (MVG) based verification in visual loop closure detection under significant illumination change, where traditional methods may fail due to their inability to either find a sufficient number of correctly matched keypoints or identify correct underlying camera motion to verify the matches. Our method is inspired by research on the spatial statistics of optical flow. By observing that the displacement of matching keypoints between a pair of images is equivalent to the optical flow under the assumption of small camera motion (which is true in applications such as loop closure detection), we exploit the fact that the displacement of correctly matched keypoints between two images must follow a well-defined distribution. This paves the way to a keypoint matching method that uses this distribution to screen or prune potential matching keypoints, so as to remove the incorrect matches (outliers) and retain the true matches (inliers) without being overly and solely dependent on keypoint descriptors. The proposed method is validated on the outdoor image sequences and shows superior performance to the standard keypoint matching method based on distance ratio test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".