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Dual Convolution Mesh Network
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README.md Initial Commit Feb 11, 2020

README.md

Dual Convolution Mesh Network (DCM Net)

prediction example

Coming soon...

Please stay tuned; we are currently working hard to get the code out quickly.

Requirements

All of our dependencies can be installed with conda or pip.

  • Python 3.7
  • Open3D
  • PyTorch 1.1 Cuda 10.0
  • TensorboardX (Tensorflow and Tensorboard are unfortunately also needed to install this)
  • Our fork of PyTorch Geometric (with its accompanying libraries as torch_scatter, torch_cluster, torch_sparse)
  • tqdm

Since we adapted PyTorch Geometric to enable graph level support, you need to install our fork as follows:

cd pytorch_geometric
python setup.py install

Preprocessing

Please refer to https://github.com/ScanNet/ScanNet to get access to the ScanNet dataset. Our method relies on the .ply as well as the .labels.ply files.

Start a new training:

python train_wrapper.py \
-c PATH_TO_EXPERIMENTS_FILE.json

Resume a training:

python train_wrapper.py \
-c PATH_TO_EXPERIMENTS_FILE.json \
-r PATH_TO_CHECKPOINT.pth

Reproduce the scores of our paper:

python run.py \
-c experiments/EXPERIMENT_NAME.json \
-r paper_checkpoints/EXPERIMENT_NAME.pth \
-e
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