Papers › Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
Detecting Vanishing Points using Global Image Context in a Non-Manhattan World
Menghua Zhai, Scott Workman, Nathan Jacobs
We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidate vanishing points, then remove outliers by enforcing mutual orthogonality. Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains. A key element of our approach is the use of global image context, extracted with a deep convolutional network, to constrain the set of candidates under consideration. Our method does not make a Manhattan-world assumption and can operate effectively on scenes with only a single horizontal vanishing point. We evaluate our approach on three benchmark datasets and achieve state-of-the-art performance on each. In addition, our approach is significantly faster than the previous best method.
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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 | CNN+FULL | AUC (horizon error) | 90.80 | #2 of 4 | Archive leaderboard | report |
| Horizon Line Estimation | Horizon Lines in the Wild | CNN+FULL | AUC (horizon error) | 58.24 | #3 of 5 | Archive leaderboard | report |
| Horizon Line Estimation | York Urban Dataset | CNN+FULL | AUC (horizon error) | 94.78 | #2 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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