用检索增强生成技术，让AI更准确地写出可执行的3D建模代码。
BlenderRAG: High-Fidelity 3D Object Generation via Retrieval-Augmented Code Synthesis
- 通过检索相似案例来辅助生成代码，减少语法错误。
- 编译成功率达70.0%，几何一致性提升至CLIP相似度0.77。
- 无需微调模型，适合快速部署在各类3D设计场景中。
从自然语言自动生成可执行的Blender代码仍具挑战性，现有大模型常出现语法错误和几何不一致问题。我们提出BlenderRAG，一种基于检索增强生成的系统，其核心为一个包含500个专家验证样本的多模态数据集（涵盖文本、代码、图像），覆盖50种物体类别。通过在生成过程中检索语义相近的示例，BlenderRAG将四种主流LLM的编译成功率从40.8%提升至70.0%，语义归一化对齐度从0.41提高到0.77（基于CLIP相似度），且无需微调或专用硬件，可立即部署。代码与数据集将开源于https://github.com/MaxRondelli/BlenderRAG。
原文摘要 · Abstract (English)
Automatic generation of executable Blender code from natural language remains challenging, with state-of-the-art LLMs producing frequent syntactic errors and geometrically inconsistent objects. We present BlenderRAG, a retrieval-augmented generation system that operates on a curated multimodal dataset of 500 expert-validated examples (text, code, image) across 50 object categories. By retrieving semantically similar examples during generation, BlenderRAG improves compilation success rates from 40.8% to 70.0% and semantic normalized alignment from 0.41 to 0.77 (CLIP similarity) across four state-of-the-art LLMs, without requiring fine-tuning or specialized hardware, making it immediately accessible for deployment. The dataset and code will be available at https://github.com/MaxRondelli/BlenderRAG.
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