End-to-End Wireframe Parsing
Yichao Zhou, Haozhi Qi, Yi Ma
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ReproduceCode
- github.com/zhou13/lcnnOfficialIn paperpytorch★ 0
Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| wireframe dataset | L-CNN | sAP5 | 58.9 | — | Unverified |
| York Urban Dataset | L-CNN | sAP5 | 24.3 | — | Unverified |