Research map / Label-efficient and robust learning / Self-supervised and contrastive
Homography Estimation From the Common Self-Polar Triangle of Separate Ellipses
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the common self-polar triangle to derive four unambiguous line correspondences | Homography estimation from two separate ellipses · Synthetic and real ellipse images | homography correspondence ambiguity | Helped |
| Add image noise when fitting the ellipses | Common self-polar triangle · Synthetic data, 0 to 0.6 pixels noise | symmetric transfer error: 0 -> 0.1639 (e, 0.2 pixels); 0 -> 0.7999 (e, 0.6 pixels) | Hurt |
| Use the common self-polar triangle method | Synthetic homography estimation · Synthetic data, no noise | homography parameter agreement with ground truth | Helped |
| Use the common self-polar triangle method | Real homography estimation · Images of ellipses on paper and oval plates | warping alignment | Helped |
Closest papers by meaning
- Noisy One-point Homographies are Surprisingly Good (CVPR 2024 · similarity 0.86)
- Three-view Focal Length Recovery From Homographies (CVPR 2025 · similarity 0.84)
- Mask-Homo: Pseudo Plane Mask-Guided Unsupervised Multi-Homography Estimation (AAAI 2024 · similarity 0.84)
- Symmetry-Robust 3D Orientation Estimation (ICML 2025 · similarity 0.82)
- Adapting Dense Matching for Homography Estimation with Grid-based Acceleration (CVPR 2025 · similarity 0.82)
- HomoMatcher: Achieving Dense Feature Matching with Semi-Dense Efficiency by Homography Estimation (AAAI 2025 · similarity 0.82)
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