论文标题
整体吸引的线框解析
Holistically-Attracted Wireframe Parsing
论文作者
论文摘要
本文提出了一种快速和简约的解析方法，可准确稳健地检测出具有单个正向通行证的输入图像中的矢量线框。所提出的方法是端到端训练，由三个组件组成：（i）线段和交界提案生成，（ii）线段和连接匹配，以及（iii）线段和连接验证。为了计算线段建议，提出了一种新颖的双重表示，该提议利用了线段的片状几何重新聚集化，并形成了输入图像的整体4维吸引场图。连接可以将其视为吸引力字段中的“盆地”。因此，所提出的方法称为整体吸引的线框解析器（HAWP）。在实验中，提出的方法在两个基准测试标准（线框数据集和约克城市数据集）上进行了测试。在两个基准上，它都在准确性和效率方面获得了最先进的性能。例如，在线框数据集上，与先前的最先进方法L-CNN相比，它可以提高挑战性的平均结构平均精度（MSAP）的大幅度（$ 2.8 \％$ $ $的绝对改进），并在单个GPU上获得29.5 fps（$ 89 \％$相对改善）。进行系统的消融研究以进一步证明所提出的方法是合理的。
This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image. Junctions can be treated as the "basins" in the attraction field. The proposed method is thus called Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban dataset. On both benchmarks, it obtains state-of-the-art performance in terms of accuracy and efficiency. For example, on the Wireframe dataset, compared to the previous state-of-the-art method L-CNN, it improves the challenging mean structural average precision (msAP) by a large margin ($2.8\%$ absolute improvements) and achieves 29.5 FPS on single GPU ($89\%$ relative improvement). A systematic ablation study is performed to further justify the proposed method.
