Papers › Deep Hough-Transform Line Priors

Deep Hough-Transform Line Priors

18 Jul 2020ECCV 2020 8arXiv:2007.09493archive 2025-07-28

Yancong Lin, Silvia L. Pintea, Jan C. van Gemert

Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants. Instead, current deep learning methods do away with all prior knowledge and replace priors by training deep networks on large manually annotated datasets. Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features. We add line priors through a trainable Hough transform block into a deep network. Hough transform provides the prior knowledge about global line parameterizations, while the convolutional layers can learn the local gradient-like line features. On the Wireframe (ShanghaiTech) and York Urban datasets we show that adding prior knowledge improves data efficiency as line priors no longer need to be learned from data. Keywords: Hough transform; global line prior, line segment detection.

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Tasks

Line Segment Detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Line Segment Detection York Urban Dataset HT-HAWP sAP10 27.4 #10 of 16 Archive leaderboard report
Line Segment Detection York Urban Dataset HT-HAWP sAP5 25.0 #10 of 16 Archive leaderboard report
Line Segment Detection wireframe dataset HT-HAWP sAP10 66.6 #3 of 10 Archive leaderboard report
Line Segment Detection wireframe dataset HT-HAWP sAP5 62.9 #3 of 10 Archive leaderboard report

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