Please cite us:
@article{1912.02984,
Author = {Qiangeng Xu and Xudong Sun and Cho-Ying Wu and Panqu Wang and Ulrich Neumann},
Title = {Grid-GCN for Fast and Scalable Point Cloud Learning},
Year = {2019},
Eprint = {arXiv:1912.02984},
Howpublished = {Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2020)}
}
make sure you have gcc version suggested by MXNET 1.5.0
cd gridifyop
vim Makefile # then change mx_home to your mxnet-apache directory, and adjust nvcc command according to your gpu model and cuda version. here we use compute power 61 and 75 for 1080 ti and 2080 ti. save the change
make
cd ..
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We refer to pointnet https://github.com/charlesq34/pointnet/blob/master/provider.py
cd data/ wget https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip unzip modelnet40_ply_hdf5_2048.zip unzip it and put it inside data/please refer to pointnet++'s github
download modelnet40_normal_resampled from https://github.com/charlesq34/pointnet2 take the modelnet10_train.txt, modelnet10_test.txt and extract from modelnet40_ply_hdf5_2048 to create a modelnet10_ply_hdf5_2048 or use modelnet40_normal_resampled directly, but configuration file configs_10.yaml new: True -> False -
Please refer to pointnet++ for downloading ScanNet use link:
# in data/ wget https://shapenet.cs.stanford.edu/media/scannet_data_pointnet2.zip unzip scannet_data_pointnet2.zip mv data scannet
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cd classification nohup python -u train/train_gpu_ggcn_mdl40.py &> mdl40.log &please refer to pointnet++
cd classification nohup python -u train/train_gpu_ggcn_mdl10.py &> mdl10.log & -
Please refer to pointnet++ for downloading ScanNet use link:
cd segmentation ### then you cd configs -> go to configs.yaml to choose 8192 points model or 81920 points model by leaving one of them uncommented nohup python -u train_test/train_ggcn_scannet.py &> train.log &
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cd segmentation ### then you cd configs -> go to configs.yaml to choose 8192 points model or 81920 points model by leaving one of them uncommented ### you should also change load_model_prefix to the intented trained model file in your output directory. nohup python -u train_test/test_ggcn_scannet.py &> test.log &