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.

中文翻译：

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点云上的自监督小样本学习

大量点云的可用性增加，加上它们在机器人、形状合成和自动驾驶汽车等各种应用中的实用性，引起了工业界和学术界越来越多的关注。最近，在标记点云上运行的深度神经网络在分类和分割等监督学习任务上显示出有希望的结果。然而，监督学习导致注释点云的繁琐任务。为了解决这个问题，我们提出了两个新颖的自监督预训练任务，它们使用覆盖树对点云的分层分区进行编码，其中点云子集位于覆盖树每个级别的不同半径的球内。此外，我们的自监督学习网络仅限于在支持集（由稀缺的训练示例组成）上进行预训练，用于在几次学习 (FSL) 设置中训练下游网络。最后，经过充分训练的自监督网络的点嵌入被输入到下游任务的网络。我们对我们的方法在下游分类和分割任务上进行了全面的实证评估，并表明使用我们的自监督学习方法预训练的监督方法显着提高了最先进方法的准确性。此外，我们的方法在下游分类任务中也优于以前的无监督方法。s 个点嵌入被输入到下游任务的网络。我们对我们的方法在下游分类和分割任务上进行了全面的实证评估，并表明使用我们的自监督学习方法预训练的监督方法显着提高了最先进方法的准确性。此外，我们的方法在下游分类任务中也优于以前的无监督方法。s 个点嵌入被输入到下游任务的网络。我们对我们的方法在下游分类和分割任务上进行了全面的实证评估，并表明使用我们的自监督学习方法预训练的监督方法显着提高了最先进方法的准确性。此外，我们的方法在下游分类任务中也优于以前的无监督方法。