CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise
  Sketch Instance Guided Attention
Mohammad Sadil Khan Elona Dupont Sk Aziz Ali Kseniya Cherenkova Anis Kacem Djamila Aouada
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.