OP-Align: Object-level and Part-level Alignment for Self-supvervised Category-level Articulated Object Pose Estimation

OP-Align is a model designed for category-level articulated object pose esimation task with self-supervised learning. This repo contains the code of OP-Align model and our real-word dataset.

You can find our paper here.

If you find the code or dataset useful for your research, please cite our paper.

@inproceedings{che2024op,
  title={OP-Align: Object-level and Part-level Alignment for Self-supvervised Category-level Articulated Object Pose Estimation},
  author={Che, Yuchen and Furukawa, Ryo and Kanezaki, Asako},
  booktitle={European Conference on Computer Vision},
  year={2024},
  organization={Springer}
}

Dataset

We provide a novel real-world dataset for category-level Articulated Object Pose Estimation task. You can download our dataset from here.

Each *.npz files contains point cloud captured from a single-view RGB-D camera. To visualize the data based on image format, reshape array into (480,640,-1).

• pc (307200, 3) # 480 * 640 * xyz
• color (307200, 3) # 480 * 640 * rgb
• detection (307200,) # 480 * 640, maskRCNN/SAM result
• segmentation (307200,) # 480 * 640, segmentation GT, 0 indicates background
• part (2, 15) # P * (9+3+3), per-part rotation, translation, scale
• joint (1, 6) # J * (3+3), per-joint direction, pivot

We also provide the HOI4D part of the synthetic dataset at here. Refer to EAP for more details. To run this dataset, add --dataset-type Light at the end of training/testing script, and change --shape-type into laptop_h or safe.

Enviroment

OP-Align uses a similar enviroment with E2PN and adds PyTorch3D module.

conda env create -f OP_environment.yml
conda activate OP
conda install -c fvcore -c iopath -c conda-forge fvcore iopath
pip install "git+https://github.com/facebookresearch/pytorch3d.git"
cd vgtk; python setup.py build_ext --inplace; cd ..
mkdir log
ln -s <dataset_location> dataset

Finanlly, we also provide the trained model weights at here. You can download and unzip them at log folder.

Running Script

Each category has different joint settings. Specifically, --shape-type indicates the category, --nmask indicates the number of parts, --njoints indicates the number of joints, --rotation-range indicates the limitation or joint movement and --joint-type indicates whether the joint is a revolute or prismatic joint. See SPConvNets/options.py for more details.

There should have a .csv file been generated at log/experiment-id folder after the testing, which contains the accuracy for each test instance.

Training

python run_art.py train experiment --experiment-id <Any_Name_You_like> --run-mode train equi_settings --shape-type basket_output --nmasks 3 --njoints 2 model --rotation-range 120 --joint-type r

python run_art.py train experiment --experiment-id <Any_Name_You_like> --run-mode train equi_settings --shape-type drawer_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type p

python run_art.py train experiment --experiment-id <Any_Name_You_like> --run-mode train equi_settings --shape-type laptop_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type r

python run_art.py train experiment --experiment-id <Any_Name_You_like> --run-mode train equi_settings --shape-type suitcase_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type r

python run_art.py train experiment --experiment-id <Any_Name_You_like> --run-mode train equi_settings --shape-type scissor_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type r

Testing

python run_art.py train --resume-path <The_Path_of_PTH_File> experiment --experiment-id <Any_Name_You_like> --run-mode test equi_settings --shape-type basket_output --nmasks 3 --njoints 2 model --rotation-range 120 --joint-type r

python run_art.py train --resume-path <The_Path_of_PTH_File> experiment --experiment-id <Any_Name_You_like> --run-mode test equi_settings --shape-type drawer_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type p

python run_art.py train --resume-path <The_Path_of_PTH_File> experiment --experiment-id <Any_Name_You_like> --run-mode test equi_settings --shape-type laptop_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type r

python run_art.py train --resume-path <The_Path_of_PTH_File> experiment --experiment-id <Any_Name_You_like> --run-mode test equi_settings --shape-type suitcase_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type r

python run_art.py train --resume-path <The_Path_of_PTH_File> experiment --experiment-id <Any_Name_You_like> --run-mode test equi_settings --shape-type scissor_output --nmasks 2 --njoints 1 model --rotation-range 120 --joint-type r

Acknowledgments

Our code is developed based on open-sourced existing works E2PN and EAP. Thanks for their great works.