CADKnitter：从文本和几何引导生成组合CAD模型
CADKnitter: Compositional CAD Generation from Text and Geometry Guidance
AI总结：
CADKnitter通过几何引导扩散策略生成组合CAD模型，结合文本和几何约束，提升多部件装配设计效率。
摘要：
构建计算机辅助设计（CAD）模型长期以来一直是耗时且繁琐的任务，需要设计师具备精确性和专业知识。随着3D生成的出现，这一任务发生了变革性影响，不仅从视觉保真度转向功能实用性，还使可编辑的CAD设计成为可能。先前的工作在单部件CAD生成方面取得了初步成功，但并不适用于现实应用，因为多个部件需要在语义和几何约束下组装。在本文中，我们提出了CADKnitter，一种具有几何引导扩散采样策略的组合CAD生成框架。CADKnitter能够生成一个符合给定CAD模型几何约束和所需设计文本提示语义约束的互补CAD部件。我们还整理了一个名为KnitCAD的数据集，包含超过310,000个CAD模型样本，以及文本提示和装配元数据，提供了语义和几何约束。大量实验表明，我们的方法在多个方面都显著优于其他最先进的基线方法。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
