Fundamental Matrix Estimation via TIP - Transfer of Invariant Parameters
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Bibliographic record
Abstract
The fundamental matrix (FM) represents the perspective transform between two or more uncalibrated images of a stationary scene, and is traditionally estimated based on 2-parameter point-to-point correspondences between image pairs. Recent invariant correspondence techniques however, provide robust correspondences in terms of 4 to 6-parameter invariant regions. Such correspondences contain important information regarding scene geometry, information which is lost in FM estimation techniques based solely on 2-parameter point translation. In this article, we present a method of incorporating this additional information into point-based FM estimation routines, entitled TIP (transfer of invariant parameters). The TIP method transforms invariant correspondence parameters into additional point correspondences, which can be used with FM estimation routines. Experimentation shows that the TIP methods result in more robust FM estimates in the case of sparse correspondence, and allows estimation based on as few as 3 correspondences in the case of affine-invariant features
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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 it