Mamba-CAD：用于3D计算机辅助设计生成建模的状态空间模型
Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modeling
AI总结：
Mamba-CAD通过自监督生成建模方法，利用更长的参数化CAD序列生成复杂的工业CAD模型。
摘要：
计算机辅助设计（CAD）生成建模在工业中具有强大而长期的应用。最近，参数化的CAD序列作为对象的设计逻辑已被序列模型广泛挖掘。然而，工业CAD模型，尤其是组件对象，是精细且复杂的，需要更长的参数化CAD序列来定义。为了解决这个问题，我们引入了Mamba-CAD，一种用于复杂CAD模型的自监督生成建模方法，能够在更长的参数化CAD序列上进行建模。具体来说，我们首先设计了一个基于Mamba架构的编码器-解码器框架，并将其与CAD重建任务配对进行预训练，以建模CAD模型的潜在表示；然后我们利用所学的表示来指导生成对抗网络生成CAD模型的假表示，最后通过MambaCAD的解码器将其恢复为参数化的CAD序列。为了训练Mamba-CAD，我们进一步创建了一个包含77,078个CAD模型和更长参数化CAD序列的新数据集。全面的实验是在各种评估指标下进行的，特别是验证有效参数化CAD序列的生成长度。代码和数据集可以从https://github.com/Sunny-Hack/Code-for-Mamba-CAD-AAAI-2025-获取。
英文摘要：
Computer-Aided Design (CAD) generative modeling has a strong and long-term application in the industry. Recently, the parametric CAD sequence as the design logic of an object has been widely mined by sequence models. However, the industrial CAD models, especially in component objects, are fine-grained and complex, requiring a longer parametric CAD sequence to define. To address the problem, we introduce Mamba-CAD, a self-supervised generative modeling for complex CAD models in the industry, which can model on a longer parametric CAD sequence. Specifically, we first design an encoder-decoder framework based on a Mamba architecture and pair it with a CAD reconstruction task for pre-training to model the latent representation of CAD models; and then we utilize the learned representation to guide a generative adversarial network to produce the fake representation of CAD models, which would be finally recovered into parametric CAD sequences via the decoder of MambaCAD. To train Mamba-CAD, we further create a new dataset consisting of 77,078 CAD models with longer parametric CAD sequences. Comprehensive experiments are conducted to demonstrate the effectiveness of our model under various evaluation metrics, especially in the generation length of valid parametric CAD sequences. The code and dataset can be achieved from https://github.com/Sunny-Hack/Code-for-Mamba-CAD-AAAI-2025-.
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。
