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End-to-End Wireframe Parsing

2019-05-08ICCV 2019Code Available0· sign in to hype

Yichao Zhou, Haozhi Qi, Yi Ma

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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.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
wireframe datasetL-CNNsAP558.9Unverified
York Urban DatasetL-CNNsAP524.3Unverified

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