提升旋转倾斜的平面图数字化精度，实现无角度依赖的结构识别。
Rotation Invariance in Floor Plan Digitization using Zernike Moments
- 用泽尼克矩特征增强平面图的旋转不变性
- 在旋转数据上实现更高F1和交并比
- 适合处理扫描老旧图纸的建筑信息提取
如今大量老旧建筑平面图以纸质形式存在或存储为扫描的位图。扫描过程中可能出现轻微旋转或偏移。将此类图像转化为机器可读格式以支持后续应用仍具挑战。为此，我们提出一个端到端流程：先对图像进行预处理，再基于预处理结果构建区域邻接图（RAG）并预测其节点。通过在RAG特征提取中引入归一化步骤，显著提升了特征计算的旋转不变性。实验表明，该方法在旋转数据上的F1分数和交并比（IoU）均有所提升。此外，我们还设计了一种墙体分割算法，可将墙体按所属房间划分为独立线段。
原文摘要 · Abstract (English)
Nowadays, a lot of old floor plans exist in printed form or are stored as scanned raster images. Slight rotations or shifts may occur during scanning. Bringing floor plans of this form into a machine readable form to enable further use, still poses a problem. Therefore, we propose an end-to-end pipeline that pre-processes the image and leverages a novel approach to create a region adjacency graph (RAG) from the pre-processed image and predict its nodes. By incorporating normalization steps into the RAG feature extraction, we significantly improved the rotation invariance of the RAG feature calculation. Moreover, applying our method leads to an improved F1 score and IoU on rotated data. Furthermore, we proposed a wall splitting algorithm for partitioning walls into segments associated with the corresponding rooms.
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