用视觉语言模型与进化算法生成拓扑正确的3D CAD模型
EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
- 结合视觉语言模型与进化优化，从符号化表示生成CAD对象
- 在CADPrompt数据集上超越已有方法，尤其在拓扑正确性上提升显著
- 提出基于欧拉示性数的两个新指标，可高效评估3D对象语义相似性
将大语言模型与进化计算相结合，能有效利用LLM的生成能力与上下文学习优势，同时发挥进化算法的优化性能。本文提出EvoCAD，一种通过符号化表示生成计算机辅助设计（CAD）对象的方法，结合视觉语言模型与推理语言模型进行进化优化。该方法首先采样多个CAD对象，再通过进化策略优化，使用GPT-4V和GPT-4o进行评估，并在CADPrompt基准数据集上与先前方法对比。此外，我们引入两个基于欧拉示性数的拓扑属性度量，用于捕捉3D对象间的语义相似性。实验结果表明，EvoCAD在多项指标上优于现有方法，尤其在生成拓扑正确对象方面表现突出，且新提出的度量可有效补充传统空间度量。
原文摘要 · Abstract (English)
Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capabilities of LLMs with the strengths of evolutionary algorithms. In this work, we present EvoCAD, a method for generating computer-aided design (CAD) objects through their symbolic representations using vision language models and evolutionary optimization. Our method samples multiple CAD objects, which are then optimized using an evolutionary approach with vision language and reasoning language models. We assess our method using GPT-4V and GPT-4o, evaluating it on the CADPrompt benchmark dataset and comparing it to prior methods. Additionally, we introduce two new metrics based on topological properties defined by the Euler characteristic, which capture a form of semantic similarity between 3D objects. Our results demonstrate that EvoCAD outperforms previous approaches on multiple metrics, particularly in generating topologically correct objects, which can be efficiently evaluated using our two novel metrics that complement existing spatial metrics.
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