Self-Supervised Few-Shot Learning on Point Clouds
- URL: http://arxiv.org/abs/2009.14168v1
- Date: Tue, 29 Sep 2020 17:32:44 GMT
- Title: Self-Supervised Few-Shot Learning on Point Clouds
- Authors: Charu Sharma, Manohar Kaul
- Abstract summary: Recently, deep neural networks operating on labeled point clouds have shown promising results on supervised learning tasks like classification and segmentation.
We propose two novel self-supervised pre-training tasks that encode a hierarchical partitioning of the point clouds.
We show that supervised methods pre-trained with our self-supervised learning method significantly improve the accuracy of state-of-the-art methods.
- Score: 18.528929583956725
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The increased availability of massive point clouds coupled with their utility
in a wide variety of applications such as robotics, shape synthesis, and
self-driving cars has attracted increased attention from both industry and
academia. Recently, deep neural networks operating on labeled point clouds have
shown promising results on supervised learning tasks like classification and
segmentation. However, supervised learning leads to the cumbersome task of
annotating the point clouds. To combat this problem, we propose two novel
self-supervised pre-training tasks that encode a hierarchical partitioning of
the point clouds using a cover-tree, where point cloud subsets lie within balls
of varying radii at each level of the cover-tree. Furthermore, our
self-supervised learning network is restricted to pre-train on the support set
(comprising of scarce training examples) used to train the downstream network
in a few-shot learning (FSL) setting. Finally, the fully-trained
self-supervised network's point embeddings are input to the downstream task's
network. We present a comprehensive empirical evaluation of our method on both
downstream classification and segmentation tasks and show that supervised
methods pre-trained with our self-supervised learning method significantly
improve the accuracy of state-of-the-art methods. Additionally, our method also
outperforms previous unsupervised methods in downstream classification tasks.
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