Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modeling

2026-02-27

AI Summary

This work addresses the challenge of generating industrial-scale, complex CAD models, whose parameter sequences are excessively long and fine-grained, rendering existing generative methods ineffective. To this end, we propose Mamba-CAD, the first approach to introduce the state space model Mamba into CAD generation. Our method employs a self-supervised encoder-decoder framework, where a latent representation is learned through CAD reconstruction pretraining. A generative adversarial network (GAN) is then leveraged to produce high-quality CAD latent codes, which are subsequently decoded into complete, parameterized sequences. We also release a new dataset comprising 77,078 complex CAD models. Experimental results demonstrate that Mamba-CAD significantly outperforms current methods across multiple metrics, particularly excelling in generating valid, long-sequence parametric CAD models.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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-.

Problem

• CAD generative modeling
• parametric CAD sequence
• complex 3D models
• sequence modeling
• industrial design

Innovation

• Mamba
• State Space Model
• CAD Generative Modeling
• Long Sequence Modeling
• Self-supervised Learning