Papers › A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws
A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws
Gilles Simon, Antoine Fond, Marie-Odile Berger
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Horizon Line Estimation | Eurasian Cities Dataset | V | AUC (horizon error) | 91.10 | #1 of 4 | Archive leaderboard | report |
| Horizon Line Estimation | Horizon Lines in the Wild | V | AUC (horizon error) | 54.43 | #5 of 5 | Archive leaderboard | report |
| Horizon Line Estimation | York Urban Dataset | V | AUC (horizon error) | 95.35 | #1 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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