首个支持可执行代码的多模态CAD数据集，含完整建模历史与验证几何。
FllumaOne: A Code-Native Multimodal CAD Dataset with Executable Programs and Kernel-Validated Feature Histories
- 用可运行的Python程序生成带建模步骤的3D模型。
- 10万样本经几何与结构验证，生成模型精度达0.002124的点云距离。
- 适合做可编辑逆向工程、程序生成和特征树预测的研究者使用。
参数化计算机辅助设计不仅记录最终几何形状，还保留决定零件可编辑性的建模操作顺序。用于可编辑CAD研究的数据集应同时暴露建模操作、参数及特征依赖关系，并附带验证后的几何信息。我们提出FllumaOne，一个以代码为本的多模态CAD数据集，其模型由基于Qt/C++ OpenCASCADE的Flluma系统中的可执行Python程序生成。每个样本对齐了程序代码、结构化特征树（训练用中间表示）、STEP几何、表面点云、自然语言描述、元数据以及八种标准可见边渲染图。主版本FllumaOne-100K包含10万份通过审核的样本，覆盖四个模板级复杂度。程序仅在通过内核几何、实体有效性及导出检查后才被保留；发布报告还记录了模态完整性与分层去重测试结果。在8万样本上训练的Qwen2.5-Coder-1.5B LoRA基线，在1万样本的测试集上达到99.98%的Python语法正确率、99.97%的Flluma构建成功率与99.14%的STEP导出有效性。对9,909个预测结果生成的表面点云，平均归一化Chamfer Distance为0.002124。该数据集支持条件化CAD重建、可执行程序合成、特征树预测、B-Rep分析、检索、设计补全与可编辑逆向工程。
原文摘要 · Abstract (English)
Parametric computer-aided design records both final geometry and the ordered construction history that determines how a part can be edited. Datasets for editable CAD research should therefore expose modeling operations, parameters, and feature dependencies together with validated geometry. We introduce FllumaOne, a code-native multimodal CAD dataset whose models are generated by executable Python programs in Flluma, a Qt/C++ OpenCASCADE-based CAD system. Each sample aligns its program with a structured feature tree, a training-oriented intermediate representation, STEP geometry, a surface point cloud, natural-language descriptions, metadata, and eight canonical visible-edge renderings. The primary release, FllumaOne-100K, contains 100,000 accepted samples across four template-level complexity regimes. Programs are executed and retained only after kernel geometry, solid validity, and export checks; release reports also record modality completeness and split-level duplicate tests. A Qwen2.5-Coder-1.5B LoRA baseline trained on 80,000 samples achieves 99.98% Python syntax validity, 99.97% Flluma build success, and 99.14% STEP-export validity on the held-out 10,000-sample test split. For the 9,909 predictions converted to surface point clouds, the mean normalized Chamfer Distance is 0.002124. The dataset supports conditioned CAD reconstruction, executable program synthesis, feature-tree prediction, B-Rep analysis, retrieval, design completion, and editable reverse engineering.
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