一个模型统一提取多种矢量结构，突破传统方法只能处理单一类型限制。
UniVector: Unified Vector Extraction via Instance-Geometry Interaction
- 用结构化查询融合实例与几何信息，通过交互模块迭代优化
- 在多结构数据集上达到新最好效果，支持多类型矢量联合提取
- 适合需要高效处理复杂矢量图的地理信息、设计建模等场景
矢量提取从位图图像中恢复结构化矢量几何，具备高保真表示和广泛适用性。现有方法通常仅针对单一矢量类型（如多边形、折线、线段），需为不同结构分别建模。根源在于将实例属性（类别、结构）与几何属性（坐标、连接关系）独立处理，难以捕捉复杂结构。受人类视觉系统同时利用语义与空间交互的启发，我们提出UniVector，一种通过实例-几何交互实现多类型矢量统一提取的框架。UniVector将矢量编码为包含实例与几何双重信息的结构化查询，并通过交互模块实现跨层级上下文交换，动态形状约束进一步优化全局结构与关键点。为评估多结构场景，我们构建了包含多样多边形、折线和线段的Multi-Vector数据集。实验表明，UniVector在单结构与多结构矢量提取任务上均达到新最优性能。代码与数据集将开源于https://github.com/yyyyll0ss/UniVector。
原文摘要 · Abstract (English)
Vector extraction retrieves structured vector geometry from raster images, offering high-fidelity representation and broad applicability. Existing methods, however, are usually tailored to a single vector type (e.g., polygons, polylines, line segments), requiring separate models for different structures. This stems from treating instance attributes (category, structure) and geometric attributes (point coordinates, connections) independently, limiting the ability to capture complex structures. Inspired by the human brain's simultaneous use of semantic and spatial interactions in visual perception, we propose UniVector, a unified VE framework that leverages instance-geometry interaction to extract multiple vector types within a single model. UniVector encodes vectors as structured queries containing both instance- and geometry-level information, and iteratively updates them through an interaction module for cross-level context exchange. A dynamic shape constraint further refines global structures and key points. To benchmark multi-structure scenarios, we introduce the Multi-Vector dataset with diverse polygons, polylines, and line segments. Experiments show UniVector sets a new state of the art on both single- and multi-structure VE tasks. Code and dataset will be released at https://github.com/yyyyll0ss/UniVector.
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