Reverse engineering in the realm of Computer-Aided Design (CAD) has been a
longstanding aspiration, though not yet entirely realized. Its primary aim is
to uncover the CAD process behind a physical object given its 3D scan. We
propose CAD-SIGNet, an end-to-end trainable and auto-regressive architecture to
recover the design history of a CAD model represented as a sequence of
sketch-and-extrusion from an input point cloud. Our model learns
visual-language representations by layer-wise cross-attention between point
cloud and CAD language embedding. In particular, a new Sketch instance Guided
Attention (SGA) module is proposed in order to reconstruct the fine-grained
details of the sketches. Thanks to its auto-regressive nature, CAD-SIGNet not
only reconstructs a unique full design history of the corresponding CAD model
given an input point cloud but also provides multiple plausible design choices.
This allows for an interactive reverse engineering scenario by providing
designers with multiple next-step choices along with the design process.
Extensive experiments on publicly available CAD datasets showcase the
effectiveness of our approach against existing baseline models in two settings,
namely, full design history recovery and conditional auto-completion from point
clouds.

给定一个三维扫描的物理对象，CAD-SIGNet 模型通过层间交叉注意力和新的 SGA 模块，能够恢复 CAD 模型的设计历史，并提供多种可能的设计选择，从而实现交互式逆向工程。在公开的 CAD 数据集上进行的实验表明，该方法在完整设计历史恢复和条件自动完成两个方面的效果优于现有的基准模型。