基于无验证器共识选择的CAD生成测试时缩放

中文摘要 AI 辅助

大型语言模型可根据自然语言描述编写参数化CAD程序（即文本到CAD生成），但单个样本往往存在错误。若要通过增加测试时计算量来采样多个候选方案，前提是能识别出优质候选，然而生成时不存在真实模型。现有系统通常需要单独的验证器（如视觉-语言评判器）来在候选间进行选择。本文探究候选池本身是否能提供足够信号以实现有效选择，提出一种无验证器替代方案：3D CAD共识选择（简称共识选择），即采样N个参数化CAD程序，将其编译为3D模型，返回与池内其余候选一致性最高的候选。该方法无需训练，可与现有CAD智能体兼容。本文探究几何与拓扑一致性概念，二者分别提升对应评估指标；在某最先进CAD生成方法的精确候选池上，几何共识在所有三项几何指标上均优于该方法的验证器，拓扑共识在拓扑指标上与验证器表现相当；在所有测试的大语言模型（LLM）及提示变体上，几何共识较从同一池随机选择提升了几何准确率，使Chamfer距离降低1%-10%。

英文摘要

Large language models can write parametric CAD programs from a natural-language description (text-to-CAD generation), but a single sample is often wrong. Increasing test-time compute by sampling multiple candidates only helps if a good candidate can be identified, yet no ground-truth model is available at generation time. Existing systems often require a separate verifier, such as a vision-language judge, to select among candidates. We investigate whether the candidate pool itself provides enough signal for effective selection and a verifier-free alternative. We introduce 3D CAD consensus selection, hereafter consensus selection: sample $N$ parametric CAD programs, compile them to 3D models, and return the candidate that agrees most with the rest of the pool. The method is training-free and compatible with existing CAD agents. We investigate geometric and topological notions of agreement, each of which improves its corresponding evaluation metric. On the exact candidate pools of a state-of-the-art CAD generation method, geometric consensus improves all three geometric metrics over the method's verifier, while topological consensus matches it on topology. Across every tested LLM and prompt variant, geometric consensus also improves geometric accuracy over random selection from the same pool, reducing Chamfer distance by $1-10\%$.

发表机构

• Siemens AG（西门子公司）

机构由 AI 辅助整理，请以论文原文为准。