用AI生成6000张真实感轮子图像和900个可分析3D模型
DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation
- 先用Stable Diffusion生成2D图像，再通过2.5D深度估计重建3D结构
- 生成超6000张照片级真实感图像，900个3D模型经结构仿真分析
- 适合做数据驱动设计、逆向设计和设计空间探索的研究者使用
数据驱动设计正成为加速工程创新的强大策略，但车辆轮子设计仍受限于缺乏大规模、高质量的包含3D几何与物理性能指标的数据集。为此，本文提出一种基于生成式AI的合成设计-性能数据集生成框架。该框架首先利用Stable Diffusion生成2D渲染图像，再通过2.5D深度估计重建3D几何结构；随后进行结构仿真以提取工程性能数据。为进一步拓展设计与性能空间，引入拓扑优化，生成更多样化的轮子设计。最终构建的DeepWheel数据集包含超过6,000张照片级真实感图像和900个经过结构分析的3D模型。该多模态数据集可用于代理模型训练、数据驱动逆向设计及设计空间探索。所提方法亦可推广至其他复杂设计领域。数据集已按CC BY-NC 4.0协议开源，可通过https://www.smartdesignlab.org/datasets获取。
原文摘要 · Abstract (English)
Data-driven design is emerging as a powerful strategy to accelerate engineering innovation. However, its application to vehicle wheel design remains limited due to the lack of large-scale, high-quality datasets that include 3D geometry and physical performance metrics. To address this gap, this study proposes a synthetic design-performance dataset generation framework using generative AI. The proposed framework first generates 2D rendered images using Stable Diffusion, and then reconstructs the 3D geometry through 2.5D depth estimation. Structural simulations are subsequently performed to extract engineering performance data. To further expand the design and performance space, topology optimization is applied, enabling the generation of a more diverse set of wheel designs. The final dataset, named DeepWheel, consists of over 6,000 photo-realistic images and 900 structurally analyzed 3D models. This multi-modal dataset serves as a valuable resource for surrogate model training, data-driven inverse design, and design space exploration. The proposed methodology is also applicable to other complex design domains. The dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International(CC BY-NC 4.0) and is available on the https://www.smartdesignlab.org/datasets
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