Structured reconstruction is a non-trivial dense prediction problem, which extracts structural information (\eg, building corners and edges) from a raster image, then reconstructs it to a 2D planar graph accordingly. Compared with common segmentation or detection problems, it significantly relays on the capability that leveraging holistic geometric information for structural reasoning. Current transformer-based approaches tackle this challenging problem in a two-stage manner, which detect corners in the first model and classify the proposed edges (corner-pairs) in the second model. However, they separate two-stage into different models and only share the backbone encoder. Unlike the existing modeling strategies, we present an enhanced corner representation method: 1) It fuses knowledge between the corner detection and edge prediction by sharing feature in different granularity; 2) Corner candidates are proposed in four heatmap channels w.r.t its direction. Both qualitative and quantitative evaluations demonstrate that our proposed method can better reconstruct fine-grained structures, such as adjacent corners and tiny edges. Consequently, it outperforms the state-of-the-art model by +1.9\%@F-1 on Corner and +3.0\%@F-1 on Edge.

翻译：结构重建是一个非平凡的密集预测问题，它从光栅图像中提取结构信息（例如建筑角点与边缘），并将其重建为对应的二维平面图。与常见的分割或检测问题相比，该问题高度依赖利用整体几何信息进行结构推理的能力。现有基于Transformer的方法以两阶段方式处理这一挑战性任务：在第一阶段模型中检测角点，在第二阶段模型中分类候选边缘（角点对）。然而，这类方法将两个阶段分离为不同模型，仅共享骨干编码器。与现有建模策略不同，我们提出了一种增强的角点表示方法：1）通过不同粒度的特征共享，融合角点检测与边缘预测之间的知识；2）角点候选通过四个热力图通道按其方向提出。定性与定量评估均表明，所提方法能更好地重建精细结构，如相邻角点与微小边缘。因此，该方法在角点评估指标上以F-1分数超过现有最优模型+1.9%，在边缘评估指标上超过+3.0%。