Abstract
We present GenCAD, an image-conditional CAD generation model. Our model not only generates the 3D CAD but also the entire parameterized CAD command history, CAD program, as output.
The complexity of CAD data structures such as boundary representation (B-rep) makes it difficult to train efficient AI models. Due to the ease of data availability, common approaches often resort to representations like meshes, voxels, or point clouds, which sacrifice the accuracy and modifiability of true CAD models that are critical for engineering tasks, manufacturing and design space exploration. Here we propose GenCAD, an image conditional generative model that generates parametric CAD command sequences, also known as CAD programs, that can be converted to a 3D solid model using a geometry kernel. At the core of GenCAD, we develop a strong representation learning framework for multiple modalities of computational engineering designs.
Our proposed GenCAD architecture is a combination of four critical steps; 1) an autoregressive transformer encoder is used for learning the latent representation of the CAD command sequences, 2) a contrastive learning-based model is used to learn the joint representations of the latent spaces between CAD command sequences and CAD-images, 3) a latent diffusion model that can generate the latent representation of CAD command sequences conditioned on CAD-images, and 4) finally, a decoder model that can convert cad latents into a sequence of parametric CAD commands. Most importantly, GenCAD does not merely generate a 3D solid but also the entire CAD program. Our work represents a step forward in CAD, offering more precise and modifiable 3D modeling from images, potentially enhancing automated design processes.
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