  3D reverse engineering, in which a CAD model is inferred given a 3D scan of a
physical object, is a research direction that offers many promising practical
applications. This paper proposes TransCAD, an end-to-end transformer-based
architecture that predicts the CAD sequence from a point cloud. TransCAD
leverages the structure of CAD sequences by using a hierarchical learning
strategy. A loop refiner is also introduced to regress sketch primitive
parameters. Rigorous experimentation on the DeepCAD and Fusion360 datasets show
that TransCAD achieves state-of-the-art results. The result analysis is
supported with a proposed metric for CAD sequence, the mean Average Precision
of CAD Sequence, that addresses the limitations of existing metrics.
