Unsupervised Multi-Task Feature Learning on Point Clouds
Abstract
We introduce an unsupervised multi-task model to jointly learn point and shape features on point clouds. We define three unsupervised tasks including clustering, reconstruction, and self-supervised classification to train a multi-scale graph-based encoder. We evaluate our model on shape classification and segmentation benchmarks. The results suggest that it outperforms prior state-of-the-art unsupervised models: In the ModelNet40 classification task, it achieves an accuracy of 89.1% and in ShapeNet segmentation task, it achieves an mIoU of 68.2 and accuracy of 88.6%.
Cite
Text
Hassani and Haley. "Unsupervised Multi-Task Feature Learning on Point Clouds." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00825Markdown
[Hassani and Haley. "Unsupervised Multi-Task Feature Learning on Point Clouds." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/hassani2019iccv-unsupervised/) doi:10.1109/ICCV.2019.00825BibTeX
@inproceedings{hassani2019iccv-unsupervised,
title = {{Unsupervised Multi-Task Feature Learning on Point Clouds}},
author = {Hassani, Kaveh and Haley, Mike},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
year = {2019},
doi = {10.1109/ICCV.2019.00825},
url = {https://mlanthology.org/iccv/2019/hassani2019iccv-unsupervised/}
}