  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%.
