This paper introduces a novel approach named CrossVideo, which aims to enhance self-supervised cross-modal contrastive learning in the field of point cloud video understanding. Traditional supervised learning methods encounter limitations due to data scarcity and challenges in label acquisition. To address these issues, we propose a self-supervised learning method that leverages the cross-modal relationship between point cloud videos and image videos to acquire meaningful feature representations. Intra-modal and cross-modal contrastive learning techniques are employed to facilitate effective comprehension of point cloud video. We also propose a multi-level contrastive approach for both modalities. Through extensive experiments, we demonstrate that our method significantly surpasses previous state-of-the-art approaches, and we conduct comprehensive ablation studies to validate the effectiveness of our proposed designs.

本文介绍了一种名为CrossVideo的新方法，旨在增强点云视频理解领域的自监督跨模态对比学习。通过利用点云视频和图像视频之间的跨模态关系获取有意义的特征表示，我们提出了一种自监督学习方法来解决数据稀缺和标签获取困难的问题。通过使用单模态和跨模态对比学习技术，我们实现了对点云视频的有效理解，并提出了一种适用于两种模态的多层对比方法。通过广泛的实验证明，我们的方法明显超过了先前的最先进方法，并进行了全面的消融研究来验证我们提出的设计的有效性。

CrossVideo: 点云视频理解的自监督跨模态对比学习