Marine surveys by robotic underwater and surface vehicles result in substantial quantities of coral reef imagery, however labeling these images is expensive and time-consuming for domain experts. Point label propagation is a technique that uses existing images labeled with sparse points to create augmented ground truth data, which can be used to train a semantic segmentation model. In this work, we show that recent advances in large foundation models facilitate the creation of augmented ground truth masks using only features extracted by the denoised version of the DINOv2 foundation model and K-Nearest Neighbors (KNN), without any pre-training. For images with extremely sparse labels, we use human-in-the-loop principles to enhance annotation efficiency: if there are 5 point labels per image, our method outperforms the prior state-of-the-art by 19.7% for mIoU. When human-in-the-loop labeling is not available, using the denoised DINOv2 features with a KNN still improves on the prior state-of-the-art by 5.8% for mIoU (5 grid points). On the semantic segmentation task, we outperform the prior state-of-the-art by 13.5% for mIoU when only 5 point labels are used for point label propagation. Additionally, we perform a comprehensive study into the number and placement of point labels, and make several recommendations for improving the efficiency of labeling images with points.

翻译：水下与水面机器人进行的海洋勘测会产生大量珊瑚礁图像，然而领域专家对这些图像进行标注既昂贵又耗时。点标签传播是一种利用现有稀疏点标注图像生成增强真值数据的技术，该数据可用于训练语义分割模型。在本研究中，我们证明，仅通过去噪版DINOv2基础模型提取的特征结合K近邻算法，无需任何预训练即可利用大型基础模型的最新进展创建增强真值掩码。对于标注极其稀疏的图像，我们采用人机协同原则提升标注效率：当每幅图像仅有5个点标签时，我们的方法在mIoU指标上超越先前最优方法19.7%。在无人机协同标注的情况下，使用去噪DINOv2特征与KNN算法仍能在mIoU指标上超越先前最优方法5.8%（5个网格点）。在语义分割任务中，当仅使用5个点标签进行点标签传播时，我们的方法在mIoU指标上超越先前最优方法13.5%。此外，我们对点标签的数量与布局进行了系统研究，并就如何提升点标注图像效率提出了若干建议。