Diffusion-CAD: Controllable Diffusion Model for Generating Computer-Aided Design Models.

Area of Science:

• Computer Graphics
• Artificial Intelligence
• Machine Learning

Background:

• Generative methods for computer-aided design (CAD) model creation are advancing rapidly.
• Current generative approaches often lack precise control over model dimensions and component relationships.

Purpose of the Study:

• To introduce Diffusion-CAD, a novel diffusion-based generative method for CAD models.
• To enable fine-grained control over the generation of CAD models, including dimensions and structural details.

Main Methods:

• Diffusion-CAD utilizes an iterative denoising process to transform Gaussian noise into continuous CAD vectors.
• These continuous vectors are then converted into discrete CAD construction sequences.
• Classifier-free and classifier-guided techniques are employed for granular control over noise, vector, and sequence distributions.

Main Results:

• Demonstrated superior performance in conditional generation tasks compared to existing methods.
• Achieved novel capabilities in controlling specific attributes of generated CAD models.
• Successfully generated CAD models with precise dimensional and structural specifications.

Conclusions:

• Diffusion-CAD offers a powerful new paradigm for controlled generative modeling in CAD.
• The method addresses key limitations in existing generative CAD approaches, enabling more detailed and accurate model creation.
• This work opens new avenues for AI-driven design and engineering applications.