Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.

水下深度预测在水下视觉研究中占有重要地位。由于水下环境复杂，获得具有可靠深度标注的水下数据集极其困难且成本高昂。因此，以数据驱动的方式估计水下深度图仍然是一项具有挑战性的任务。为了解决这个问题，我们提出了一个端到端系统，包括分别用于水下图像合成和水下深度图估计的两个不同模块。前一个模块旨在将模糊的空中 RGB-D 图像转换为多风格逼真的合成水下图像，同时保留输入图像的对象和结构信息。然后我们使用合成的水下图像和原始相应的深度图构建半真实的 RGB-D 水下数据集。我们进行监督学习，通过伪配对的水下 RGB-D 图像进行深度估计。综合实验表明，该方法可以生成多幅逼真的高保真水下图像，可用于提高单目水下图像深度估计的性能。此外，训练后的深度估计模型可以应用于真实的水下图像深度图估计。我们将在 训练好的深度估计模型可以应用于真实的水下图像深度图估计。我们将在 训练好的深度估计模型可以应用于真实的水下图像深度图估计。我们将在https://github.com/ZHAOQIII/UW_depth.