Mamba-CAD: State Space Model for 3D Computer-Aided Design Generative Modeling
Authors: Xueyang Li, Yunzhong Lou, Yu Song, Xiangdong Zhou
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | Xueyang Li, Yunzhong Lou, Yu Song, Xiangdong Zhou B School of Computer Science, Fudan University, Shanghai, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The pseudo code of Mamba-CAD can be found in the uploaded Technical Appendix . |
| Open Source Code | No | The paper states it will make its *dataset* publicly available in the future, but there is no explicit statement or link indicating that the *code* for the methodology described in this paper is currently available. |
| Open Datasets | No | Finally, total 77,078 CAD models described as parametric CAD sequences are grouped into a new dataset that will be released publicly to promote future research on the parametric CAD modeling. ...To train Mamba-CAD, we further construct a new dataset of parametric CAD sequences that is more complex than the existing benchmark dataset of the same type and will make it publicly available in the future. |
| Dataset Splits | Yes | Finally, total 77,078 CAD models are collected as represented in parametric CAD sequences, in which 61,662 CAD models for training, 7,707 models for validation and 7,709 models for testing. |
| Hardware Specification | Yes | The pre-training is conducted on one NVIDIA RTX 3090 GPU with the batch size of 32 under 10 epochs in about 5 hours. |
| Software Dependencies | No | The paper mentions several software-related concepts and frameworks (e.g., Transformer, Mamba, PyTorch implicitly through loss functions), but does not provide specific version numbers for any libraries, programming languages, or other software components. |
| Experiment Setup | Yes | The pre-training is conducted on one NVIDIA RTX 3090 GPU with the batch size of 32 under 10 epochs in about 5 hours. The initial learning rate is set to 0.001 with warmup (He et al. 2016) and gradient clipping of 1.0 is applied in back-propagation. ...train a 1-D Latent GAN(Chen and Zhang 2019) with 200,000 epochs and batch size of 256 in about 3 hours. |