Awesome

PSGMN

code for paper "Pseudo-Siamese Graph Matching Network for Textureless Objects' 6D Pose Estimation". If you find this code useful for your research, please consider citing our paper with the following BibTeX entry.

@ARTICLE{psgmn,
  author={C. {Wu} and L. {Chen} and Z. {He} and J. {Jiang}},
  journal={IEEE Transactions on Industrial Electronics}, 
  title={Pseudo-Siamese Graph Matching Network for Textureless Objects' 6D Pose Estimation}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TIE.2021.3070501}}

Installation

1. Set up the python environment:

   conda create -n psgmn python=3.7
   conda activate psgmn
   

   install torch 1.5 built for cuda 10.1

   conda install pytorch==1.5.0 torchvision==0.6.1 cudatoolkit=10.1 -c pytorch
   

   install pytorch_geometric

   pip install torch-scatter==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.5.0.html
   pip install torch-sparse==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.5.0.html
   pip install torch-cluster==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.5.0.html
   pip install torch-spline-conv==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.5.0.html
   pip install torch-geometric
   

   install other requirements

   pip install -r requirements.txt
   

   compile the cuda extension

   cd csrc
   python setup.py build_ext --inplace 
   

2. Set up datasets: Download datasets which are formatted by PVNet:

   (1). linemod

   (2). occlusion linemod

   Download the simplified mesh models for each object here. Unzip the file and copy it to linemode dataset.

   Make soft links to the datasets.

   ROOT=/path/to/gsgmn
   cd $ROOT
   mkdir data
   cd data
   ln -s /path/to/linemod linemod
   ln -s /path/to/occlusion_linemod occlusion_linemod
   

Training

Take the training on ape as an example. run

python main_psgmn.py --class_type ape --train True

Testing

Testing on Linemod

We provide the pretrained models of objects on Linemod, which can be found at here.

Take the testing on ape as an example.

1. Download the pretrained model of ape and put it to $ROOT/model/ape/200.pkl.
2. Test:

   python main_psgmn.py --class_type ape --eval True
   python main_psgmn.py --class_type ape --occ True --eval True