CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding
This work addresses challenges in point cloud video understanding for applications like robotics and autonomous driving, representing an incremental advancement in self-supervised learning methods.
The paper tackles the problem of data scarcity and label acquisition in point cloud video understanding by proposing CrossVideo, a self-supervised cross-modal contrastive learning method that leverages relationships between point cloud and image videos, resulting in significant performance improvements over previous state-of-the-art approaches.
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.