SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design

Abstract

We introduce SldprtNet, a large-scale dataset comprising over 242,000 industrial parts, designed for semantic-driven CAD modeling, geometric deep learning, and the training/fine-tuning of multimodal models for 3D design. The dataset provides 3D models in both .step and .sldprt formats to support di-verse training and testing. To enable parametric modeling and facilitate dataset scalability, we developed two supporting tools, encoder and decoder, which support 13 types of CAD commands and enable lossless transformation between 3D models and a structured text representation. Additionally, each sample is paired with a composite image created by merging seven rendered views from different view-points of the 3D model, effectively reducing input token length and accelerating inference. By combining this image with the parameterized text output from the encoder, we employ the lightweight multi-modal language model Qwen2.5-VL-7B to generate a natural language description of each part’s appearance and functionality. To ensure accuracy, we manually verified and aligned the generated descriptions, rendered images, and 3D models. These descriptions, along with the parameterized modeling scripts, rendered images, and 3D model files, are fully aligned to construct SldprtNet. To assess its effectiveness, we fine-tuned baseline models on a dataset subset, com-paring image-plus-text inputs with text-only inputs. Results confirm the necessity and value of multi-modal datasets for CAD generation. It features care-fully selected real-world industrial parts, supporting tools for scalable dataset expansion, diverse modalities, and ensured diversity in model complexity and geometric features, making it a comprehensive multimodal dataset built for semantic-driven CAD modeling and cross-modal learning.