In this paper, we develop a new method to automatically convert 2D line drawings from three orthographic views into 3D CAD models. Existing methods for this problem reconstruct 3D models by back-projecting the 2D observations into 3D space while maintaining explicit correspondence between the input and output. Such methods are sensitive to errors and noises in the input, thus often fail in practice where the input drawings created by human designers are imperfect. To overcome this difficulty, we leverage the attention mechanism in a Transformer-based sequence generation model to learn flexible mappings between the input and output. Further, we design shape programs which are suitable for generating the objects of interest to boost the reconstruction accuracy and facilitate CAD modeling applications. Experiments on a new benchmark dataset show that our method significantly outperforms existing ones when the inputs are noisy or incomplete.

翻译：本文提出一种新方法，可自动将三个正交视图中的二维线图转换为三维CAD模型。现有方法通过将二维观测反投影至三维空间来重建三维模型，同时保持输入与输出间的显式对应关系。这类方法对输入中的错误和噪声敏感，因此在人工设计师绘制的输入图纸不完美的实际场景中常以失败告终。为解决这一难题，我们利用基于Transformer的序列生成模型中的注意力机制，学习输入与输出间的灵活映射。此外，我们设计了适用于生成目标对象的形状程序，以提升重建精度并促进CAD建模应用。在新型基准数据集上的实验表明，当输入存在噪声或不完整时，本方法显著优于现有方法。