Giter VIP home page Giter VIP logo

grid-gcn's Introduction

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  &
    

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.