GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors

• URL: http://arxiv.org/abs/2409.16294v1
• Date: Sun, 8 Sep 2024 23:49:11 GMT
• Title: GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors
• Authors: Md Ferdous Alam, Faez Ahmed
• Abstract summary: GenCAD is a generative model that transforms image inputs into parametric CAD command sequences. It significantly outperforms existing state-of-the-art methods in terms of the precision and modifiability of generated 3D shapes.
• Score: 4.485378844492069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The creation of manufacturable and editable 3D shapes through Computer-Aided Design (CAD) remains a highly manual and time-consuming task, hampered by the complex topology of boundary representations of 3D solids and unintuitive design tools. This paper introduces GenCAD, a generative model that employs autoregressive transformers and latent diffusion models to transform image inputs into parametric CAD command sequences, resulting in editable 3D shape representations. GenCAD integrates an autoregressive transformer-based architecture with a contrastive learning framework, enhancing the generation of CAD programs from input images and providing a representation learning framework for multiple data modalities relevant to engineering designs. Extensive evaluations demonstrate that GenCAD significantly outperforms existing state-of-the-art methods in terms of the precision and modifiability of generated 3D shapes. Notably, GenCAD shows a marked improvement in the accuracy of 3D shape generation for long sequences, supporting its application in complex design tasks. Additionally, the contrastive embedding feature of GenCAD facilitates the retrieval of CAD models using image queries from databases which is a critical challenge within the CAD community. While most work in the 3D shape generation literature focuses on representations like meshes, voxels, or point clouds, practical engineering applications demand modifiability and the ability for multi-modal conditional generation. Our results provide a significant step forward in this direction, highlighting the potential of generative models to expedite the entire design-to-production pipeline and seamlessly integrate different design modalities.

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