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

TL;DR

Mamba-CAD introduces a self-supervised generative model that effectively handles complex, long parametric CAD sequences, improving the generation of valid 3D CAD models for industrial applications.

Contribution

The paper presents a novel encoder-decoder framework with a Mamba architecture and a new dataset, enabling modeling and generation of longer, complex CAD sequences.

Findings

Effective generation of longer CAD sequences

High validity of generated models

Superior performance on evaluation metrics

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-.