论文标题
TP-LSD：基于三点的线段检测器
TP-LSD: Tri-Points Based Line Segment Detector
论文作者
论文摘要
本文提出了一种新型的深卷积模型，基于三点的线段检测器（TP-LSD），以实时速度检测图像中的线段。以前的相关方法通常使用两步策略，依赖于启发式后过程或额外的分类器。为了通过更快，更紧凑的模型实现一步检测，我们介绍了三点表示，将线段检测转换为根点的端到端预测，每个线段段的两个端点。 TP-LSD有两个分支：三点提取分支和线分割分支。前者预测根点的热图和端点的两个位移图。后者段将像素从背景上直线排出。此外，线分割图在第一个分支中以结构性先验重复使用。我们提出了一个其他新颖的评估指标，并在线框和约克城数据集上评估了我们的方法，不仅证明了与最新方法相比的竞争精度，而且还证明了实时运行速度高达78 fps，并具有320美元的320美元输入。
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.
