Robust homography for real-time image un-distortion
Bibliographic record
Abstract
Stereoscopic 3D film production has increased the need for efficient and robust camera calibration and tracking. Many of these tasks involve making planar correspondence and thus accurate fast homography estimation is essential. However, homography estimation may fail with distorted images since the planar projected corners may be distorted far away from the “perfect” locations. On the other hand, precisely estimating lens distortion from a single image is still a challenge, especially in real-time applications. In this paper, we drop the assumption that the image distortion is negligible in homography estimation. We propose robust homography as a simple and efficient approach which combines homography mapping and image distortion estimation in a least square constraint. Our method can simultaneously estimate homography and image distortion from a single image in real-time. Compared with previous methods, it has two advantages: first, un-distortion can be achieved with little overhead due to the need for only a single calibration image and the real-time homography mapping of easy to track corners; second, due to the use of precise calibration targets the accuracy of our method is comparable to the multiple image calibration methods. In an experimental evaluation, we show that our method can accurately estimate image distortion parameters in both synthetic and real images. We also present its applications in close range un-distortion and robust corner detection.
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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 | 0.001 |
| 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".