论文标题
信息3D：使用共同信息最大化和对比度学习对3D对象的表示形式学习
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning
论文作者
论文摘要
计算机视觉的主要努力是从3D数据中表示，理解和提取结构。为了实现这一目标，无监督的学习是一种强大而必要的工具。大多数用于3D形状分析的无监督方法都使用对齐的数据集，需要重建对象，并且在下游任务上的性能恶化。为了解决这些问题，我们建议在3D形状上扩展信息和对比度学习原则。我们表明，我们可以最大化3D对象及其“块”之间的共同信息，以改善对齐数据集中的表示形式。此外，我们可以通过最大化3D对象之间的相互信息及其几何变换版本来实现So $ {（3）} $组中的旋转不变性。最后，我们进行了几项实验，例如聚类，转移学习，塑造检索并实现最先进的结果。
A major endeavor of computer vision is to represent, understand and extract structure from 3D data. Towards this goal, unsupervised learning is a powerful and necessary tool. Most current unsupervised methods for 3D shape analysis use datasets that are aligned, require objects to be reconstructed and suffer from deteriorated performance on downstream tasks. To solve these issues, we propose to extend the InfoMax and contrastive learning principles on 3D shapes. We show that we can maximize the mutual information between 3D objects and their "chunks" to improve the representations in aligned datasets. Furthermore, we can achieve rotation invariance in SO${(3)}$ group by maximizing the mutual information between the 3D objects and their geometric transformed versions. Finally, we conduct several experiments such as clustering, transfer learning, shape retrieval, and achieve state of art results.
