Recovering Computer-Aided Design (CAD) programs from 3D geometries is a widely studied problem. Recent advances in large language models (LLMs) have enabled progress in CAD program synthesis, but existing methods rely on supervised training with paired shape-program data, which is often unavailable. We introduce PLLM, a self-training framework for CAD program synthesis from unlabeled 3D shapes. Given a pre-trained CAD-capable LLM and a shape dataset, PLLM iteratively samples candidate programs, selects high-fidelity executions, and augments programs to construct synthetic program-shape pairs for fine-tuning. We experiment on adapting CAD-Recode from DeepCAD to the unlabeled ABC dataset show consistent improvements in geometric fidelity and program diversity.

翻译：从三维几何形状中恢复计算机辅助设计（CAD）程序是一个被广泛研究的问题。大型语言模型（LLM）的最新进展推动了CAD程序合成领域的发展，但现有方法依赖于需要成对形状-程序数据的监督训练，而此类数据往往难以获取。本文提出PLLM，一种针对无标注三维形状进行CAD程序合成的自训练框架。给定一个预训练的具备CAD能力的LLM和一个形状数据集，PLLM迭代地采样候选程序，选择高保真度的执行结果，并通过程序增广来构建用于微调的合成程序-形状对。我们在将DeepCAD中的CAD-Recode适配到无标注ABC数据集上的实验表明，该方法在几何保真度和程序多样性方面均取得了持续提升。