论文标题
技术图的深度矢量化
Deep Vectorization of Technical Drawings
论文作者
论文摘要
我们提出了一种用于矢量化技术线条图的新方法，例如平面图，建筑图纸和2D CAD图像。我们的方法包括（1）基于深度学习的清洁阶段，以消除图像中的背景和瑕疵并填充缺失的零件，（2）基于变压器的网络估算向量原始词，以及（3）优化程序以获得最终的原始配置。我们培训网络的合成数据，矢量线图的效果图以及对线图的手动矢量扫描。我们的方法在定量和定性上的表现在代表性技术图集中的许多现有技术胜过。
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-based network to estimate vector primitives, and (3) optimization procedure to obtain the final primitive configurations. We train the networks on synthetic data, renderings of vector line drawings, and manually vectorized scans of line drawings. Our method quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings.
