  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.
