UnsupervisedR&R: Unsupervised Pointcloud Registration via Differentiable Rendering

This repository holds all the code and data for our recent work on unsupervised point cloud registration:

UnsupervisedR&R: Unsupervised Pointcloud Registration via Differentiable Rendering
Mohamed El Banani, Luya Gao, Justin Johnson

If you find this code useful, please consider citing:

@inProceedings{elbanani2021unsupervisedrr,
  title={UnsupervisedR&R: Unsupervised Pointcloud Registration via Differentiable Rendering},
  author={El Banani, Mohamed and Gao, Luya and Johnson, Justin},
  year={2021},
}

If you have any questions about the paper or the code, please feel free to email me at [email protected]

Usage Instructions

1. How to setup your environment?
2. How to download and setup the datasets?
3. How to train models?
4. How to run inference with pretrained checkpoints?

Acknowledgments

We would like to thank the reviewers and area chairs for their valuable comments and suggestions. We also thank Nilesh Kulkarni, Karan Desai, Richard Higgins, and Max Smith for many helpful discussions and feedback on early drafts of this work.

We would also like to acknowledge the following repositories and users for making great code openly available for us to use:

• @pytorch/pytorch because I can't imagine doing this in CAFFE.
• @facebookresearch/pytorch3d for a great 3D vision library.
• @intel-isl/Open3D for easy to use implementations of traditional methods and great visualizers.
• @chrischoy and @zgojcic for releasing excellent code that allows for easy geometric registration.