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.

该论文介绍了一种新的方法，可以自动将来自三个正交视图的2D线条图转换为3D CAD模型。该方法利用基于Transformer的序列生成模型中的注意机制，学习输入到输出之间的灵活映射，通过设计适用于生成感兴趣对象的形状程序来提高重建精度并促进CAD建模应用。在一个新的基准数据集上的实验证明，当输入有噪声或不完整时，我们的方法明显优于现有方法。

PlankAssembly：基于三个正交视图和学习形状程序的鲁棒三维重构