This paper proposes a novel deep convolutional model, Tri-Points Based Line
Segment Detector (TP-LSD), to detect line segments in an image at real-time
speed. The previous related methods typically use the two-step strategy,
relying on either heuristic post-process or extra classifier. To realize
one-step detection with a faster and more compact model, we introduce the
tri-points representation, converting the line segment detection to the
end-to-end prediction of a root-point and two endpoints for each line segment.
TP-LSD has two branches: tri-points extraction branch and line segmentation
branch. The former predicts the heat map of root-points and the two
displacement maps of endpoints. The latter segments the pixels on straight
lines out from background. Moreover, the line segmentation map is reused in the
first branch as structural prior. We propose an additional novel evaluation
metric and evaluate our method on Wireframe and YorkUrban datasets,
demonstrating not only the competitive accuracy compared to the most recent
methods, but also the real-time run speed up to 78 FPS with the $320\times 320$
input.

本研究提出了一种新型的深度卷积模型 TP-LSD，采用三点表示法用于图像线段检测，并通过定义新的评估指标以 Wireframe 和 YorkUrban 数据集为基础，验证其运行速度高达每秒 78 帧，具有竞争性的准确性和结构先验。