ACPV-Net：面向航空影像无缝矢量地图生成的全类别多边形矢量化
ACPV-Net: All-Class Polygonal Vectorization for Seamless Vector Map Generation from Aerial Imagery
面向航空影像的「全类别多边形矢量化」任务与 Deventer-512 基准，其共享边界、无缝隙无重叠的拓扑约束与语义监督条件化机制，可迁移到建筑提取、地物制图等需要输出矢量多边形的视觉任务。
原文依据 producing polygons for all land-cover classes with shared boundaries and without gaps or overlaps
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论本文研究从航空影像一次性生成完整矢量地图表示的问题：为所有土地覆盖类别生成共享边界、无缝隙且无重叠的多边形。现有多边形化方法通常是类别特定的；通过逐类别运行将其扩展到多类别时，往往会导致拓扑不一致，例如重复边、缝隙和重叠。我们将这一新任务形式化为全类别多边形矢量化（ACPV），并发布首个公开基准 Deventer-512，其标准化指标联合评估语义保真度、几何精度、顶点效率、逐类别拓扑保真度和全局拓扑一致性。为实现 ACPV，我们提出 ACPV-Net，这是一个统一框架，引入新颖的语义监督条件化（SSC）机制，将语义感知与几何基元生成相耦合，并通过设计强制共享边一致性的拓扑重建。在施加如此严格拓扑约束的同时，ACPV-Net 在 Deventer-512 上于各类别均超越了所有类别特定基线。它无需任何架构修改即可应用于单类别多边形矢量化，在 WHU-Building 上取得已报告的最佳结果。数据、代码和模型将发布于：https://github.com/HeinzJiao/ACPV-Net。
英文原文摘要 1382 字符
We tackle the problem of generating a complete vector map representation from aerial imagery in a single run: producing polygons for all land-cover classes with shared boundaries and without gaps or overlaps. Existing polygonization methods are typically class-specific; extending them to multiple classes via per-class runs commonly leads to topological inconsistencies, such as duplicated edges, gaps, and overlaps. We formalize this new task as All-Class Polygonal Vectorization (ACPV) and release the first public benchmark, Deventer-512, with standardized metrics jointly evaluating semantic fidelity, geometric accuracy, vertex efficiency, per-class topological fidelity and global topological consistency. To realize ACPV, we propose ACPV-Net, a unified framework introducing a novel Semantically Supervised Conditioning (SSC) mechanism coupling semantic perception with geometric primitive generation, along with a topological reconstruction that enforces shared-edge consistency by design. While enforcing such strict topological constraints, ACPV-Net surpasses all class-specific baselines in polygon quality across classes on Deventer-512. It also applies to single-class polygonal vectorization without any architectural modification, achieving the best-reported results on WHU-Building. Data, code, and models will be released at: https://github.com/HeinzJiao/ACPV-Net.