TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds

Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis, Gleb A Gusev, Anis Kacem, Djamila Aouada

Abstract:

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 [42] and Fusion360 [40] 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.