Research StatementI do research in shape analysis and geometric deep learning. My work focuses on equivariance properties of neural networks,
but I have a broad interest in artificial intelligence and machine learning.
Recent Publications
Poulenard, A., & Guibas, L. J. (2021). A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13174-13183).
@inproceedings{pg-fnltfn-21,
title={A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks.},
author={Poulenard, Adrien and Guibas, Leonidas J},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={13174--13183},
year={2021}
}
R.Sajnani, A.Poulenard, J.Jain, R.Dua, L.Guibas, and S.Sridhar, ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes, arXiv preprint arXiv:2201.07788, (2022).
@article{spjdgs-condor-22,
title={ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes},
author={Sajnani, Rahul and Poulenard, Adrien and Jain, Jivitesh and Dua, Radhika and Guibas, Leonidas J and Sridhar, Srinath},
journal={arXiv preprint arXiv:2201.07788},
year={2022}
}
|
 |
|||
| Copyright 2025 Guibas Laboratory. All rights reserved. Manage Publications | ||||
