You can create and activate a conda environment for this project using the following commands:

conda env create -f environment.yml
conda activate WaveletGeneration
pip install git+https://github.com/fbcotter/pytorch_wavelets

For the dataset, we use the data split provided by IM-NET for training and evaluation. For ease of data preparation, we provided pre-computed coarse and detail coefficient volumes in this link, and you can unzip it into wavelet_data folder.

For the data processing, it involves running 4 files in order: flip_axis.py, to_manifold.py, sample_sdf.py and convert_wavelet.py. These four files will flip the orientation of the mesh, convert them into watertight meshes, sampling the sdf values and finally convert it to wavelet coefficients.

To prepare the dataset, you must first download the ShapeNetV1 dataset from https://shapenet.org/.

Before running flip_axis.py, set the ShapeNet root folder in the root variable.

To run to_manifold.py, you must first compile the library in external/Manifold according to the instructions given in https://github.com/hjwdzh/Manifold. After that, also set the ShapeNet root folder in the root variable.

To run sample_sdf.py, you need to install a modified version of mesh_to_sdf by running:

cd external/mesh_to_sdf
pip install .

Before running sample_sdf.py, set the ShapeNet root folder in the data_folder variable, and set the folder to save SDF samples in the save_folder variable. To choose which objects to sample SDF values, specify a txt filename in the obj_names_path variable. You can follow the name format from IM-NET, or use the names provided in our data processing here.

Finally, you can run convert_wavelet.py after setting the folder to save SDF samples in the sdf_save_folder variable, the folder to save the wavelet coefficients in the npy_save_folder variable, and the ShapeNet category ID in the category_id variable. Note that setting resolution_index to 3 generates the coarse wavelet coefficients, while setting it to 2 generates the detail wavelet coefficients.

Our training and inference mainly depend on the config file (config.py or config_highs.py).

Before running the training, you should specify the location of the data in the config files. In particular, you can need to set data_filesthe path of two npy files downloaded or created, and an example is shown as follow:

data_files = [('wavelet_data/03001627_0.1_bior6.8_3_zero.npy', 3), ('wavelet_data/03001627_0.1_bior6.8_2_zero.npy', 2)]

For the training of diffusion model, you can run the following command:

python trainer/trainer.py --resume_path ./configs/config.py

For the training of detail predictor, you can run the following command:

python trainer/trainer.py --resume_path ./configs/config_highs.py

We provided our pre-trained models for our method. You can unzip the file (link) in the pretrain folder. Note that the pre-trained models are trained with a setting slightly different from the original one (mainly on the batch size, learning rate, and training iteration), but the performance should be compatible.

To run the inference for different categories, you need to edit models/network_gen.py and set <category> of diffusion_folder and high_level_folder to one of the four categories (chair, table, airplane and cabinet). Also, you need to set the epoch numbers of pretrained models in epoch and high_level_epoch.

After setting the above, you run the inference by running:

python models/network_gen.py

Note that if you inference your own trained model, you need to set the above variables according to your training output folders and epoch numbers.

[27/3/2023 Update] We also provide the original pre-trained models of the chair and airplane categories in (link). Besides, we provide the generated meshes of the (chair) and (airplane).

you need to set PYTHONPATH=<project root directory> before running any above commands.

If you find our work useful in your research, please consider citing:

@article{hui2022wavelet,
    title = {Neural Wavelet-domain Diffusion for 3D Shape Generation},
    author = {Ka-Hei Hui and Ruihui Li and Jingyu Hu and Chi-Wing Fu},
    joural = {SIGGRAPH Asia 2022 Conference Papers},
    month = {December},
    year = {2022},
}