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Unsupervised Multi-Task Feature Learning on Point Clouds

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If you have a question about this talk, please contact James Fergusson .

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%. (based on: https://arxiv.org/pdf/1910.08207.pdf)

This talk is part of the Data Intensive Science Seminar Series series.

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