用点云方法精准识别大尺寸CAD图中的符号，支持自动建模。
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings
- 将图形转为带坐标的密集点云，统一建模提升鲁棒性。
- 在1000㎡大图纸上实现高精度分割，准确率显著优于现有方法。
- 适用于建筑自动化设计，适合做智能建模的开发者参考。
我们提出CADSpotting，一种针对大规模建筑CAD图纸的全景符号检测方法。现有方法常因符号多样性、尺度变化和重叠元素而表现不佳，且依赖额外特征（如原始图元类型或图层）提升性能。CADSpotting通过仅使用坐标属性的密集采样点表示图元，采用统一3D点云模型进行鲁棒特征学习。为实现大图精确分割，提出新型滑动窗口聚合（SWA）技术，结合加权投票与非极大值抑制（NMS）。此外，构建了新数据集LS-CAD，包含45个精细标注的平面图，每张覆盖约1000 $m^2$，远超以往基准。该数据集将公开。在FloorPlanCAD与LS-CAD上的实验表明，CADSpotting显著优于现有方法。我们还展示了其实际价值：可直接从原始CAD输入实现参数化三维室内重建。
原文摘要 · Abstract (English)
We introduce CADSpotting, an effective method for panoptic symbol spotting in large-scale architectural CAD drawings. Existing approaches often struggle with symbol diversity, scale variations, and overlapping elements in CAD designs, and typically rely on additional features (e.g., primitive types or graphical layers) to improve performance. CADSpotting overcomes these challenges by representing primitives through densely sampled points with only coordinate attributes, using a unified 3D point cloud model for robust feature learning. To enable accurate segmentation in large drawings, we further propose a novel Sliding Window Aggregation (SWA) technique that combines weighted voting and Non-Maximum Suppression (NMS). Moreover, we introduce LS-CAD, a new large-scale dataset comprising 45 finely annotated floorplans, each covering approximately 1,000 $m^2$, significantly larger than prior benchmarks. LS-CAD will be publicly released to support future research. Experiments on FloorPlanCAD and LS-CAD demonstrate that CADSpotting significantly outperforms existing methods. We also showcase its practical value by enabling automated parametric 3D interior reconstruction directly from raw CAD inputs.
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