CAD Translator: An Effective Drive for Text to 3D Parametric Computer-Aided Design Generative Modeling

Computer-Aided Design (CAD) generative modeling is widely applicable in the fields of industrial engineering. Recently, text-to-3D generation has shown rapid progress in point clouds, mesh, and other non-parametric representations. On the contrary, text to 3D parametric CAD generative modeling is a more appealing task in industry but has not been well explored. The parametric CAD model means the product shape can be defined by using the command sequences of CAD tools. To investigate this, we design an encoder-decoder framework, namely CAD Translator, for incorporating the embedding of parametric CAD sequences into texts appropriately with only one-stage training. We first align texts and parametric CAD sequences via a Cascading Contrastive Strategy in the latent space, and then we propose CT-Mix to conduct the random mask operation on their embeddings separately to further get a fusion embedding via the linear interpolation. This can strengthen the connection between texts and parametric CAD sequences effectively. To train CAD Translator, we build a Text2CAD dataset with the help of Large Multimodal Model (LMM) and conduct thorough experiments to demonstrate the effectiveness of our method.