End-to-End Wireframe Parsing
This addresses the problem of extracting geometrically accurate wireframes for computer vision applications, though it appears incremental as it builds on existing wireframe parsing work.
The paper tackles wireframe detection in images by introducing an end-to-end trainable algorithm that directly outputs vectorized wireframes, outperforming previous state-of-the-art methods in experiments.
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.