A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws
We show that, in images of man-made environments, the horizon line can usually be hypothesized based on an a contrario detection of second-order grouping events. This allows constraining the extraction of the horizontal vanishing points on that line, thus reducing false detections. Experiments made on three datasets show that our method, not only achieves state-of-the-art performance w.r.t. horizon line detection on two datasets, but also yields much less spurious vanishing points than the previous top-ranked methods.
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Datasets
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Horizon Line Estimation | Eurasian Cities Dataset | V | AUC (horizon error) | 91.10 | # 1 | |
| Horizon Line Estimation | Horizon Lines in the Wild | V | AUC (horizon error) | 54.43 | # 5 | |
| Horizon Line Estimation | York Urban Dataset | V | AUC (horizon error) | 95.35 | # 1 |
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