This repository provides the code for creating the CONG dataset from the paper Constrained Generative Sampling of 6-DoF Grasps.
Create the conda environment:
conda env create -f environment.yml
Run the script using the command python main.py
with the following options:
-mp, --mesh_path
: Path to meshes (default:data/shapenetsem_example_meshes/
)-gp, --grasp_path
: Path to acronym grasps (default:data/acronym_example_grasps/
)-sp, --folder_for_storing
: Path where to save results (default:/tmp/constrained_grasping_dataset/
)-np, --number_of_points_to_sample_on_mesh
: Number of points to sample on the mesh (default: 1024)-th, --threshold
: Threshold between center of grasps and query points (default: 0.002)-nq, --num_query_points
: Number of query points to sample from the mesh (default: 50)-v, --visualize
: Flag to visualize the results
Example:
python main.py -v
- Download the full acronym dataset: acronym.tar.gz
- Download the ShapeNetSem meshes from https://www.shapenet.org/
python main.py -mp path/to/ShapeNetSem/meshes -gp path/to/Acronym/dataset
Feel free to open issues, suggest enhancements, or make pull requests.
If this code is useful in your research, please consider citing:
@article{lundell2023constrained,
title={Constrained generative sampling of 6-dof grasps},
author={Lundell, Jens and Verdoja, Francesco and Le, Tran Nguyen and Mousavian, Arsalan and Fox, Dieter and Kyrki, Ville},
journal={arXiv preprint arXiv:2302.10745},
year={2023}
}