Papers › End-to-End Wireframe Parsing
End-to-End Wireframe Parsing
Yichao Zhou, Haozhi Qi, Yi Ma
We present a conceptually simple yet effective algorithm to detect wireframes in a given image. Compared to the previous methods which first predict an intermediate heat map and then extract straight lines with heuristic algorithms, our method is end-to-end trainable and can directly output a vectorized wireframe that contains semantically meaningful and geometrically salient junctions and lines. To better understand the quality of the outputs, we propose a new metric for wireframe evaluation that penalizes overlapped line segments and incorrect line connectivities. We conduct extensive experiments and show that our method significantly outperforms the previous state-of-the-art wireframe and line extraction algorithms. We hope our simple approach can be served as a baseline for future wireframe parsing studies. Code has been made publicly available at https://github.com/zhou13/lcnn.
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Code
Syntology Ran 1 of 14 code samples harvested from 1 repository linked to this paper; 13 have no recorded run. Of those that ran: 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
14 samples harvested; 1 ran; 0 honoured the contract we drafted; 13 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Line Segment Detection | York Urban Dataset | L-CNN | sAP10 | 26.4 | #12 of 16 | Archive leaderboard | report |
| Line Segment Detection | York Urban Dataset | L-CNN | sAP5 | 24.3 | #12 of 16 | Archive leaderboard | report |
| Line Segment Detection | wireframe dataset | L-CNN | sAP10 | 62.9 | #5 of 10 | Archive leaderboard | report |
| Line Segment Detection | wireframe dataset | L-CNN | sAP15 | 64.7 | #5 of 10 | Archive leaderboard | report |
| Line Segment Detection | wireframe dataset | L-CNN | sAP5 | 58.9 | #5 of 10 | 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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