用文本和几何约束生成可组装的3D零件，提升设计效率。
CADKnitter: Compositional CAD Generation from Text and Geometry Guidance
- 通过几何引导扩散采样生成匹配已有模型的零件
- 在31万样本数据集上实现超越现有方法的生成质量
- 适合需要快速生成可装配3D零件的工业设计人员
计算机辅助设计（CAD）以紧凑、精确且可编辑的方式定义3D模型，在多个领域具有直接应用价值。近年来，CAD生成受到研究界和产业界的广泛关注。传统手工建模耗时费力，需设计者具备高精度与专业技能。早期工作仅能生成单个部件，难以满足实际应用中多部件需符合语义约束与几何兼容性的需求。本文提出CADKnitter，一种基于几何引导提示的组合式CAD生成框架，可生成既符合给定模型几何约束，又满足文本语义要求的互补零件。我们还构建了名为KnitCAD的数据集，包含超过310,000个带文本提示和装配元信息的CAD模型，提供语义与几何双重约束。大量实验表明，所提方法显著优于当前最优基线。项目主页见https://cadknitter.github.io/。
原文摘要 · Abstract (English)
Computer-aided design (CAD) defines 3D models as compact, precise, and editable representations, making it directly useful for several fields. Recently, CAD generation has been gaining more attention in both the research community and industry. Crafting CAD models has long been a painstaking and time-intensive task, demanding both precision and expertise from designers. Prior works have achieved early success in single-part CAD generation, which is not well-suited for real-world applications, as multiple parts need to be assembled under semantic constraints and geometric compatibility. In this paper, we propose CADKnitter, a compositional CAD generation framework with geometric-guiding cues to steer diffusion sampling. CADKnitter is able to generate a complementary CAD part that follows both the geometric constraints of the given CAD model and the semantic constraints of the desired design text prompt. We also curate a dataset, so-called KnitCAD, containing over 310,000 samples of CAD models, along with textual prompts and assembly metadata that provide semantic and geometric constraints. Intensive experiments demonstrate that our proposed method outperforms other state-of-the-art baselines by a clear margin. Our project page is available at https://cadknitter.github.io/.
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