OpenECAD: An Efficient Visual Language Model for Editable 3D-CAD Design

• URL: http://arxiv.org/abs/2406.09913v3
• Date: Tue, 6 Aug 2024 08:27:55 GMT
• Title: OpenECAD: An Efficient Visual Language Model for Editable 3D-CAD Design
• Authors: Zhe Yuan, Jianqi Shi, Yanhong Huang,
• Abstract summary: We fine-tuned pre-trained models to create OpenECAD models (0.55B, 0.89B, 2.4B and 3.1B) OpenECAD models can process images of 3D designs as input and generate highly structured 2D sketches and 3D construction commands. These outputs can be directly used with existing CAD tools' APIs to generate project files.
• Score: 1.481550828146527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computer-aided design (CAD) tools are utilized in the manufacturing industry for modeling everything from cups to spacecraft. These programs are complex to use and typically require years of training and experience to master. Structured and well-constrained 2D sketches and 3D constructions are crucial components of CAD modeling. A well-executed CAD model can be seamlessly integrated into the manufacturing process, thereby enhancing production efficiency. Deep generative models of 3D shapes and 3D object reconstruction models have garnered significant research interest. However, most of these models produce discrete forms of 3D objects that are not editable. Moreover, the few models based on CAD operations often have substantial input restrictions. In this work, we fine-tuned pre-trained models to create OpenECAD models (0.55B, 0.89B, 2.4B and 3.1B), leveraging the visual, logical, coding, and general capabilities of visual language models. OpenECAD models can process images of 3D designs as input and generate highly structured 2D sketches and 3D construction commands, ensuring that the designs are editable. These outputs can be directly used with existing CAD tools' APIs to generate project files. To train our network, we created a series of OpenECAD datasets. These datasets are derived from existing public CAD datasets, adjusted and augmented to meet the specific requirements of vision language model (VLM) training. Additionally, we have introduced an approach that utilizes dependency relationships to define and generate sketches, further enriching the content and functionality of the datasets.

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