Subdivision-based Mesh Convolutional Networks

The official implementation of SubdivNet in our paper,

Subdivion-based Mesh Convolutional Networks

Requirements

• python3.7+
• CUDA 10.1+
• Jittor

To install python requirements:

pip install -r requirements.txt

Fetch Data

This repo provides training scripts for classification and segementation, on the following datasets,

• shrec11-split10
• shrec11-split16
• cubes
• manifold40 (based on ModelNet40)
• humanbody
• coseg-aliens

To download the preprocessed data, run

sh scripts/<DATASET_NAME>/get_data.sh

Manfold40 (before remeshed) can be downloaded via this link.

Training

To train the model(s) in the paper, run this command:

sh scripts/<DATASET_NAME>/train.sh

To speed up training, you can use multiple gpus. First install OpenMPI:

sudo apt install openmpi-bin openmpi-common libopenmpi-dev

Then run the following command,

CUDA_VISIBLE_DEVICES="2,3" mpirun -np 2 sh scripts/<DATASET_NAME>/train.sh

Evaluation

To evaluate the model on a dataset, run:

sh scripts/<DATASET_NAME>/test.sh

The pretrained weights are provided. Run the following command to download them.

sh scripts/<DATASET_NAME>/get_pretrained.sh

Visualize

After testing the segmentation network, there will be colored shapes in a results directory. Use your favorite 3D viewer to check them.

Apply to your own data

To create your own data with subdivision sequence connectivity, you may use the provided tool that implements the MAPS algorithm. You may also refer to NeuralSubdivision, as they provide a MATLAB scripts for remeshing.

To run our implemented MAPS algorithm, first install the following python dependecies,

triangle
pymeshlab
shapely
sortedcollections
networkx
rtree

Then see datagen_maps.py and modify the configurations to remesh your 3D shapes for subdivision connectivity.

Cite

Please cite our paper if you use this code in your own work:

@misc{hu2021subdivisionbased,
      title={Subdivision-Based Mesh Convolution Networks}, 
      author={Shi-Min Hu and Zheng-Ning Liu and Meng-Hao Guo and Jun-Xiong Cai and Jiahui Huang and Tai-Jiang Mu and Ralph R. Martin},
      year={2021},
      eprint={2106.02285},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}