PolarSym：面向CAD平面图解析的极坐标几何感知注意力机制
PolarSym: Polar Geometry-aware Attention for CAD Floorplan Parsing
- School of Computer and Information Engineering, Henan University of Economics and Law（河南财经政法大学计算机与信息工程学院）
- Third Dimension (Henan) Software Technology（第三维度（河南）软件科技公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有Transformer方法在CAD平面图解析中几何对称性建模不足的问题，提出PolarSym极坐标几何感知注意力框架，解耦方向与距离建模，在公开数据集上较SymPoint V2基线实现多项指标提升且收敛更快。
AI中文摘要：
CAD平面图解析是建筑信息建模（BIM）中的一项基础任务，旨在从二维工程图中自动提取墙体、门、窗、家具等建筑元素。现有基于Transformer的方法通过自注意力捕获全局语义依赖，但仅从语义特征推断空间关系，未明确表征建筑布局固有的几何对称性，这类方法在长程匹配和复杂对称空间布局中易产生不匹配对应关系。为解决该局限，我们提出PolarSym，一种面向CAD平面图解析的极坐标几何感知注意力框架。该框架将建筑的几何关系解耦为方向和距离两个互补分量，分别建模；通过方向约束强化结构一致性，通过距离约束建立长程对称对应关系；采用动态门控机制协同融合两个几何信息分支，同时保持标准Transformer架构。该设计以可忽略的额外计算量提升几何建模能力。在公开CAD平面图解析数据集上的实验显示，在相同训练设置下，PolarSym较复现的SymPoint V2基线方法，PQ提升1.73%、RQ提升1.54%、mIoU提升4.31%；PolarSym收敛更快且优化更稳定。消融实验验证了方向与距离建模的互补效应。我们的结果表明，PolarSym以低计算成本提升了Transformer的几何感知能力，为CAD平面图解析提供了一种有效的几何建模范式。
英文摘要：
CAD plan parsing is a fundamental task in Building Information Modeling (BIM), aiming to automatically extract architectural elements including walls, doors, windows, and furniture from 2D engineering drawings. Existing Transformer-based methods capture global semantic dependencies via self-attention, yet they infer spatial relationships merely from semantic features without explicitly characterizing the intrinsic geometric symmetry of building layouts. Such methods tend to produce mismatched correspondences in long-range matching and complex symmetric spatial layouts. To tackle this limitation, we propose PolarSym, a polar-coordinate geometry-aware attention framework for CAD plan parsing. The framework decouples geometric relationships of buildings into two complementary components, direction and distance, which are modeled independently. Structural consistency is strengthened by directional constraints, while long-range symmetric correspondences are built with distance constraints. A dynamic gating mechanism is adopted to synergistically fuse the two geometric information branches while maintaining the vanilla Transformer architecture. This design boosts geometric modeling capacity with negligible extra computation. Experiments on a public CAD plan parsing dataset show that PolarSym surpasses the reproduced SymPoint V2 baseline by 1.73% PQ, 1.54% RQ and 4.31% mIoU under identical training settings. PolarSym also converges faster and yields more stable optimization. Ablation experiments verify the complementary effects of direction and distance modeling. Our results reveal that PolarSym improves the geometric awareness of Transformers at low computational cost, offering an effective geometric modeling paradigm for CAD plan parsing.
