The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer from relatively small training sets, creating the need for statistically efficient methods to learn 3D shape representations. In this paper, we investigate the use of Approximate Convex Decompositions (ACD) as a self-supervisory signal for label-efficient learning of point cloud representations. We show that using ACD to approximate ground truth segmentation provides excellent self-supervision for learning 3D point cloud representations that are highly effective on downstream tasks. We report improvements over the state-of-the-art for unsupervised representation learning on the ModelNet40 shape classification dataset and significant gains in few-shot part segmentation on the ShapeNetPart dataset.Code available at https://github.com/matheusgadelha/PointCloudLearningACD

中文翻译：

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使用近似凸分解的点云标签高效学习

在过去几年中，3D 点云的形状分类和零件分割问题引起了越来越多的关注。然而，这两个问题都受到相对较小的训练集的影响，因此需要使用统计有效的方法来学习 3D 形状表示。在本文中，我们研究了使用近似凸分解 (ACD) 作为自我监督信号，用于点云表示的标签高效学习。我们表明，使用 ACD 来近似地面实况分割为学习 3D 点云表示提供了出色的自我监督，这些表示对下游任务非常有效。