https://github.com/Xharlie/Grid-GCN

Grid-GCN for Fast and Scalable Point Cloud Learning
https://github.com/Xharlie/Grid-GCN

3d-vision 3dvision classification computer-vision cvpr2020 graphconv graphconvoltution grid-gcn point-cloud pointnet s3dis scannet segmentation self-driving-car

Last synced: about 2 months ago
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Grid-GCN for Fast and Scalable Point Cloud Learning

• Host: GitHub
• URL: https://github.com/Xharlie/Grid-GCN
• Owner: Xharlie
• Created: 2020-03-03T07:02:40.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-03-19T07:13:52.000Z (over 4 years ago)
• Last Synced: 2024-07-22T02:31:41.138Z (about 2 months ago)
• Topics: 3d-vision, 3dvision, classification, computer-vision, cvpr2020, graphconv, graphconvoltution, grid-gcn, point-cloud, pointnet, s3dis, scannet, segmentation, self-driving-car
• Language: Python
• Size: 6.38 MB
• Stars: 182
• Watchers: 11
• Forks: 25
• Open Issues: 11
• Metadata Files:
  • Readme: README.md

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README

        
# Grid-GCN for Fast and Scalable Point Cloud Learning (CVPR2020)

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)}

}

``` 

## Requirement: GGCN implemented by MXNET 1.5.0

make sure you have gcc version suggested by MXNET 1.5.0

## Install Our CUDA modules to MXNET Libary:

```

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 ..

```

## Data Preparation

* ### Classification

  * #### ModelNet40

  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/

  ```

  * #### ModelNet10

  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

  ```

  

* ### Segmentation/ScanNet

  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

## Training

* ### Classification

  * #### ModelNet40

  ```

  cd classification

  nohup python -u train/train_gpu_ggcn_mdl40.py &> mdl40.log & 

  

  ```

  * #### ModelNet10

  please refer to pointnet++

  ```

  cd classification

  nohup python -u train/train_gpu_ggcn_mdl10.py &> mdl10.log &

  

  ```

  

* ### Segmentation 

  * #### ScanNet

  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  &

  ```

## Testing

* ### Segmentation

  * #### ScanNet

  ```

  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  &

  ```