We introduces BRep-GD, a novel graph diffusion model designed to address the challenges of topological validity and efficient generation in complex CAD Boundary Representation modeling. By representing B-reps as graph structures and incorporating a continuous topology decoupled diffusion mechanism, BRep-GD achieves simultaneous generation of topological and geometric features, significantly improving the efficiency and quality of B-rep model generation.

We will release the code after the paper is accepted.

Requirements

Environment (Tested)

• Linux
• Python 3.11
• CUDA 11.8
• PyTorch 2.5
• Diffusers 0.27

Dependencies

Install PyTorch and other dependencies:

conda create --name brepgen_env python=3.9 -y
conda activate brepgen_env

pip install -r requirements.txt
pip install torch_geometric
pip install pyg_lib torch_scatter torch_sparse torch_cluster 
pip install chamferdist

If chamferdist fails to install here are a few options to try:

• If there is a CUDA version mismatch error, then try setting the CUDA_HOME environment variable to point to CUDA installation folder. The CUDA version of this folder must match with PyTorch's version i.e. 11.8.

• Try building from source.

Install OCCWL following the instruction here. If conda is stuck in "Solving environment..." there are two options to try:

• Try using mamba as suggested in occwl's README.

• Install pythonOCC: https://github.com/tpaviot/pythonocc-core?tab=readme-ov-file#install-with-conda and occwl manually: pip install git+https://github.com/AutodeskAILab/occwl.

Training

Train the surface and edge VAE (wandb for logging):

sh train_vae.sh

Train the latent diffusion model (change path to previously trained VAEs):

sh train_ldm.sh

--cf classifier-free training for the CadNet40v2 and Furniture dataset.

--data_aug randomly rotate the CAD model during training (optional).

Generation and Evaluation

Randomly generate B-reps from Gaussian noise, both STEP and STL files will be saved:

python sample.py --mode abc

This will load the settings in eval_config.yaml. Make sure to update model paths to the correct folder.

Run this script for evaluation (change the path to generated data folder, with at least 3,000 samples):

sh eval.sh