Neural subdivision subdivides a triangle mesh using neural networks. This is a prototype implementation in Python 3.7 with PyTorch 1.3.1 and MATLAB. The Python code requires standard dependencies (e.g., numpy), and the MATLAB code depends on gptoolbox.
For a quick demo, please use the pre-trained model and test on new shapes. To test the pre-tranied model please run python test.py /path/to/model/folder/ /path/to/test.obj. For instance, you can run
python test.py ./jobs/net_cartoon_elephant/ ./data_meshes/objs/bunny.obj
If you would like to re-train a model, please first generate a dataset in the form of, for instance, ./data_meshes/cartoon_elephant_200/. This could be done by running the MATLAB script genTrainData_slow.m (a faster C++ version for generating training data can be found here).
Once you have the dataset, please run python gendataPKL.py to preprocess the meshes into a .pkl file, where you need to specify the folder that contains the mesh (please refer to gendataPKL.py for more detail).
The next step is to use python writeHyperparam.py to create a folder that contains the parameters of the model (see writeHyperparam.py for more detail). In our example code, running python writeHyperparam.py will create a folder named ./jobs/net_cartoon_elephant/ which contains the model parameters.
Then you can run python train.py /path/to/model/folder/ to train the model. For instance, with the default folder generated with the above script, you can simply run python train.py ./jobs/net_cartoon_elephant/ to train the model. After training, you can use the quick demo code test.py to test the model by running python test.py /path/to/model/folder/ /path/to/testMesh.obj.
If any questions, please contact Hsueh-Ti Derek Liu (hsuehtil@cs.toronto.edu).