ZS6D

We demonstrate the effectiveness of deep features extracted from self-supervised, pre-trained Vision Transformer (ViT) for Zero-shot 6D pose estimation. For more detailed information check out the corresponding [paper].

Installation:

Docker setup:

ROS integration:

Template rendering:

To generate templates from a object model to perform inference, we refer to the ZS6D_template_rendering repository.

Template preparation:

Inference:

Evaluation on BOP Datasets:

Acknowledgements

This project is built upon dino-vit-features, which performed a very comprehensive study about features of self-supervised pretrained Vision Transformers and their applications, including local correspondence matching. Here is a link to their paper. We thank the authors for their great work and repo.

Citation

If you found this repository useful please consider starring ⭐ and citing :

@article{ausserlechner2023zs6d,
  title={ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers},
  author={Ausserlechner, Philipp and Haberger, David and Thalhammer, Stefan and Weibel, Jean-Baptiste and Vincze, Markus},
  journal={arXiv preprint arXiv:2309.11986},
  year={2023}
}