用3D生成模型快速设计汽车，自动估算风阻等性能。
VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling
- 基于DeepSDF的潜空间生成汽车3D模型。
- 可快速估算风阻等性能参数，支持高效优化。
- 适合汽车设计、生成式工程优化领域研究人员。
机械设计中的主要挑战是在满足工程约束的前提下高效探索设计空间。本文探讨了使用3D生成模型在车辆开发中探索设计空间并估计与强制执行工程约束的方法。具体而言，我们生成符合特定几何规格的多样化汽车3D模型，同时快速估算空气动力学阻力等性能参数。为此，我们采用数据驱动方法（使用ShapeNet数据集）训练VehicleSDF——一个基于DeepSDF的模型，将潜在设计表示在潜空间中，并可解码为3D模型。随后，我们训练代理模型，从潜空间表示中估计工程参数，从而实现对潜向量的高效优化以匹配指定条件。实验表明，我们能生成多样化的3D模型并准确匹配几何参数。最后，我们展示了其他性能参数（如空气动力学阻力）可在可微分流程中被有效估算。
原文摘要 · Abstract (English)
A main challenge in mechanical design is to efficiently explore the design space while satisfying engineering constraints. This work explores the use of 3D generative models to explore the design space in the context of vehicle development, while estimating and enforcing engineering constraints. Specifically, we generate diverse 3D models of cars that meet a given set of geometric specifications, while also obtaining quick estimates of performance parameters such as aerodynamic drag. For this, we employ a data-driven approach (using the ShapeNet dataset) to train VehicleSDF, a DeepSDF based model that represents potential designs in a latent space witch can be decoded into a 3D model. We then train surrogate models to estimate engineering parameters from this latent space representation, enabling us to efficiently optimize latent vectors to match specifications. Our experiments show that we can generate diverse 3D models while matching the specified geometric parameters. Finally, we demonstrate that other performance parameters such as aerodynamic drag can be estimated in a differentiable pipeline.
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