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

TL;DR

This work introduces Mamba-CAD, a self-supervised generative modeling for complex CAD models in the industry, which can model on a longer parametric CAD sequence, and utilizes the learned representation to guide a generative adversarial network to produce the fake representation of CAD models.

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-.

Figures (5)

• Figure 1: The gallery of CAD models created by Mamba-CAD, including visual illustrations of the created CAD models from CAD sequence reconstruction, completion, and random generation.
• Figure 2: Common CAD commands discussed in this paper. $<$SOL$>$ and $<$EOS$>$ are two symbols for representing the start of one loop and the end of a whole parametric CAD sequence, respectively. $f$ is a counter-clockwise flag.
• Figure 3: The overview of our Mamba-CAD architecture. The whole pipeline can be divided into three parts which marked in three different colored line-arrows. (Pre-training stage $\longrightarrow$): We leverage a task of CAD sequence reconstruction to pre-train the encoder and decoder of Mamba-CAD. Given a mini-batch of CAD-models, their corresponding parametric CAD sequences would be first discretized into friendly network representations (Sec. CAD sequence representation \ref{['sec2']}). Next, these representations keep going forward Fusion Embedding, Encoder, and Decoder of Mamba-CAD (Sec. Mamba-CAD \ref{['sec3']}) to recover predicted parametric CAD sequences. (Training stage $\longrightarrow$): When the pre-training stage is complete, the encoder parameters of Mamba-CAD would be frozen to produce the latent representation $\mathbb{K}$ that is further used to guide a 1-D Latent-GAN training. (Generation stage $\longrightarrow$): Once the 1-D Latent GAN is well-trained, it enables to randomly sample the noise from a standard gaussian distribution and further feed it into Generator to produce the fake latent representation $\mathbb{F}$, which would further undergo the decoder of Mamba-CAD to recover the parametric CAD sequence.
• Figure 4: Visual illustrations of CAD sequence reconstruction. GT represents Ground Truth. $\emptyset$ denotes the CAD sequence is invalid, leading to the failure of constructing a 3D shape. Please enlarge 200% for a clear view.
• Figure 5: Visualizations of random generation of parametric CAD sequences.