This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a learning-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. Furthermore, for sensitive tasks like inspecting cracks, photorealistic mapping is very important. However, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.

翻译：本文提出了一种基于学习图像增强方法和网格地图表示的逼真实时稠密三维地图构建系统。由于水下环境中存在雾化、低对比度等问题，传统的同时定位与地图构建（SLAM）方法难以直接应用。此外，在裂缝检测等敏感任务中，逼真地图构建至关重要。然而，自主水下机器人（AUV）的计算资源受限。本文采用基于神经网络的图像增强方法提升位姿估计与地图构建质量，并提出基于滑动窗口的网格扩展方法，实现轻量化、快速且逼真的地图构建。为验证所提方法，我们使用真实世界数据集和室内合成数据集：通过真实数据集进行定性验证，并通过将室内合成数据集建模为水下场景进行定量验证。