CADTrans: A code tree-guided CAD generative transformer model with regularized discrete codebooks

Abstract

The creation of computational agents capable of generating computer-aided design (CAD) models that rival those produced by professional designers is a pressing challenge in the field of computational design. The key obstacle is the need to generate a large number of realistic and diverse models while maintaining control over the output to a certain degree. Therefore, we propose a novel CAD model generation network called CADTrans which is based on a code tree-guided transformer framework to autoregressively generate CAD construction sequences. Firstly, three regularized discrete codebooks are extracted through vector quantized adversarial learning, with each codebook respectively representing the features of Loop, Profile, and Solid. Secondly, these codebooks are used to normalize a CAD construction sequence into a structured code tree representation which is then used to train a standard transformer network to reconstruct the code tree. Finally, the code tree is used as global information to guide the sketch-and-extrude method to recover the corresponding geometric information, thereby reconstructing the complete CAD model. Extensive experiments demonstrate that CADTrans achieves state-of-the-art performance, generating higher-quality, more varied, and complex models. Meanwhile, it provides more possibilities for CAD applications through its flexible control method, enabling users to quickly experiment with different design schemes, inspiring diverse design ideas and the generation of a wide variety of models or even inspiring models, thereby improving design efficiency and promoting creativity. The code is available at https://effieguoxufei.github.io/CADtrans/.