A Synthetic CAD Models Dataset for Deep Learning

In recent years, deep learning methods (DL) have exhibited significant potential in the field of CAD, but there is currently a lack of large-scale CAD model datasets that support deep learning for parametrized feature-based modeling.

Therefore, we synthesized a large-scale dataset containing one million CAD designs to provide labeled CAD model data for supervised training in relevant deep learning tasks.We employed a random synthesis algorithm to generate CAD models and documented the corresponding feature-based modeling processes, including principal primitives (cuboids, prisms, cylinders, cones, and spheres) and detail features (slots, semi-circular slots, through-holes, steps, fillets, chamfers, etc.).