修复结构化图像中多边形分割的拓扑错误，提升标注一致性。
Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains
- 通过修复索引空间的相邻关系保持多边形拓扑
- 在常见几何变换下实现接近完美的循环邻接保留（CAP）
- 轻量级设计，可无缝集成到现有预处理流程
几何数据增强广泛用于分割流程，但通常假设多边形标注在变换后仍有效。这一假设在结构化领域（如建筑平面图分析）中可能失效，因为一个区域可能包含作为单个有序多边形链的内部空洞。裁剪或裁剪操作可能移除该链中的桥接顶点，导致单一语义区域分裂为不连通部分。本文提出一种轻量级拓扑保持增强策略，在不改变原始顶点顺序的前提下修复索引空间中缺失的邻接关系。该方法开销极小，可集成至现有预处理流程。实验表明，所提方法在常见几何变换下实现了接近完美的循环邻接保留（CAP），并提升了基于多边形的分割中标注的一致性。
原文摘要 · Abstract (English)
Geometric data augmentation is widely used in segmentation workflows, but polygon annotations are often assumed to remain valid after transformation. This assumption can fail in structured domains such as architectural floorplan analysis, where a region may contain an interior void encoded as part of a single ordered polygon chain. Cropping or clipping can remove bridge vertices in this chain, causing one semantic region to split into disconnected components. We propose a lightweight topology-preserving augmentation strategy that repairs missing adjacency relations in index space while preserving the original vertex order. The method adds minimal overhead and can be integrated into existing preprocessing workflows. Experiments show that the proposed approach achieves near-perfect Cyclic Adjacency Preservation (CAP) across common geometric transformations and improves annotation consistency in polygon-based segmentation.
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