Papers › Line Segment Detection Using Transformers without Edges
Line Segment Detection Using Transformers without Edges
Yifan Xu, Weijian Xu, David Cheung, Zhuowen Tu
In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. Our method, named LinE segment TRansformers (LETR), takes advantages of having integrated tokenized queries, a self-attention mechanism, and an encoding-decoding strategy within Transformers by skipping standard heuristic designs for the edge element detection and perceptual grouping processes. We equip Transformers with a multi-scale encoder/decoder strategy to perform fine-grained line segment detection under a direct endpoint distance loss. This loss term is particularly suitable for detecting geometric structures such as line segments that are not conveniently represented by the standard bounding box representations. The Transformers learn to gradually refine line segments through layers of self-attention. In our experiments, we show state-of-the-art results on Wireframe and YorkUrban benchmarks.
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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 | LETR | FH | 66.9 | #14 of 16 | Archive leaderboard | report |
| Line Segment Detection | York Urban Dataset | LETR | sAP10 | 29.4 | #14 of 16 | Archive leaderboard | report |
| Line Segment Detection | York Urban Dataset | LETR | sAP15 | 31.7 | #14 of 16 | Archive leaderboard | report |
| Multi-Task Learning | wireframe dataset | LETR | FH | 83.3 | #1 of 1 | Archive leaderboard | report |
| Multi-Task Learning | wireframe dataset | LETR | sAP10 | 65.2 | #1 of 1 | Archive leaderboard | report |
| Multi-Task Learning | wireframe dataset | LETR | sAP15 | 67.7 | #1 of 1 | 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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