用自训练方法从无标注3D模型生成CAD程序，提升精度与多样性。
PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis
- 基于预训练模型迭代生成候选程序并筛选高保真执行结果
- 在ABC数据集上几何保真度和程序多样性均持续提升
- 适合缺乏配对数据的CAD程序自动合成场景
从3D几何体恢复计算机辅助设计（CAD）程序是一个广泛研究的问题。大型语言模型（LLM）的进展推动了CAD程序合成的突破，但现有方法依赖成对的形状-程序数据进行监督训练，这类数据往往难以获取。本文提出PLLM，一种从无标注3D形状进行CAD程序合成的自训练框架。给定一个具备CAD能力的预训练语言模型和一个形状数据集，PLLM通过迭代采样候选程序、选择高保真执行结果，并扩充程序以构建合成的程序-形状对用于微调。在将DeepCAD的CAD-Recode适配到无标注的ABC数据集上的实验表明，几何保真度和程序多样性均获得持续提升。
原文摘要 · Abstract (English)
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.
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